{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "main.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "mount_file_id": "1Ng-nKz4ZE746xO6k8OXYYa4Md1JRNWSA",
      "authorship_tag": "ABX9TyOe3m9F3PBhwK9qigXpg10v",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    },
    "widgets": {
      "application/vnd.jupyter.widget-state+json": {
        "cacc943016394d5a9addb3defd1c3170": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "HBoxModel",
          "state": {
            "_view_name": "HBoxView",
            "_dom_classes": [],
            "_model_name": "HBoxModel",
            "_view_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_view_count": null,
            "_view_module_version": "1.5.0",
            "box_style": "",
            "layout": "IPY_MODEL_6a753e8f2f874049bd5b12eeef39861f",
            "_model_module": "@jupyter-widgets/controls",
            "children": [
              "IPY_MODEL_946da29893d744d5a5d8b463d9a896b5",
              "IPY_MODEL_b7d72c607bbb468686614ec7309aa8c5"
            ]
          }
        },
        "6a753e8f2f874049bd5b12eeef39861f": {
          "model_module": "@jupyter-widgets/base",
          "model_name": "LayoutModel",
          "state": {
            "_view_name": "LayoutView",
            "grid_template_rows": null,
            "right": null,
            "justify_content": null,
            "_view_module": "@jupyter-widgets/base",
            "overflow": null,
            "_model_module_version": "1.2.0",
            "_view_count": null,
            "flex_flow": null,
            "width": null,
            "min_width": null,
            "border": null,
            "align_items": null,
            "bottom": null,
            "_model_module": "@jupyter-widgets/base",
            "top": null,
            "grid_column": null,
            "overflow_y": null,
            "overflow_x": null,
            "grid_auto_flow": null,
            "grid_area": null,
            "grid_template_columns": null,
            "flex": null,
            "_model_name": "LayoutModel",
            "justify_items": null,
            "grid_row": null,
            "max_height": null,
            "align_content": null,
            "visibility": null,
            "align_self": null,
            "height": null,
            "min_height": null,
            "padding": null,
            "grid_auto_rows": null,
            "grid_gap": null,
            "max_width": null,
            "order": null,
            "_view_module_version": "1.2.0",
            "grid_template_areas": null,
            "object_position": null,
            "object_fit": null,
            "grid_auto_columns": null,
            "margin": null,
            "display": null,
            "left": null
          }
        },
        "946da29893d744d5a5d8b463d9a896b5": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "FloatProgressModel",
          "state": {
            "_view_name": "ProgressView",
            "style": "IPY_MODEL_f35fc7f2ca904a3b89a561b80b842793",
            "_dom_classes": [],
            "description": "Dask Apply: 100%",
            "_model_name": "FloatProgressModel",
            "bar_style": "success",
            "max": 4,
            "_view_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "value": 4,
            "_view_count": null,
            "_view_module_version": "1.5.0",
            "orientation": "horizontal",
            "min": 0,
            "description_tooltip": null,
            "_model_module": "@jupyter-widgets/controls",
            "layout": "IPY_MODEL_63177b3db8724ac194f1d11902924a37"
          }
        },
        "b7d72c607bbb468686614ec7309aa8c5": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "HTMLModel",
          "state": {
            "_view_name": "HTMLView",
            "style": "IPY_MODEL_8bc09170cf7d46788a6fa285c5917d68",
            "_dom_classes": [],
            "description": "",
            "_model_name": "HTMLModel",
            "placeholder": "​",
            "_view_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "value": " 4/4 [08:40&lt;00:00, 130.22s/it]",
            "_view_count": null,
            "_view_module_version": "1.5.0",
            "description_tooltip": null,
            "_model_module": "@jupyter-widgets/controls",
            "layout": "IPY_MODEL_e5c99098c0ac4ab383d0fdda2c9f560a"
          }
        },
        "f35fc7f2ca904a3b89a561b80b842793": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "ProgressStyleModel",
          "state": {
            "_view_name": "StyleView",
            "_model_name": "ProgressStyleModel",
            "description_width": "initial",
            "_view_module": "@jupyter-widgets/base",
            "_model_module_version": "1.5.0",
            "_view_count": null,
            "_view_module_version": "1.2.0",
            "bar_color": null,
            "_model_module": "@jupyter-widgets/controls"
          }
        },
        "63177b3db8724ac194f1d11902924a37": {
          "model_module": "@jupyter-widgets/base",
          "model_name": "LayoutModel",
          "state": {
            "_view_name": "LayoutView",
            "grid_template_rows": null,
            "right": null,
            "justify_content": null,
            "_view_module": "@jupyter-widgets/base",
            "overflow": null,
            "_model_module_version": "1.2.0",
            "_view_count": null,
            "flex_flow": null,
            "width": null,
            "min_width": null,
            "border": null,
            "align_items": null,
            "bottom": null,
            "_model_module": "@jupyter-widgets/base",
            "top": null,
            "grid_column": null,
            "overflow_y": null,
            "overflow_x": null,
            "grid_auto_flow": null,
            "grid_area": null,
            "grid_template_columns": null,
            "flex": null,
            "_model_name": "LayoutModel",
            "justify_items": null,
            "grid_row": null,
            "max_height": null,
            "align_content": null,
            "visibility": null,
            "align_self": null,
            "height": null,
            "min_height": null,
            "padding": null,
            "grid_auto_rows": null,
            "grid_gap": null,
            "max_width": null,
            "order": null,
            "_view_module_version": "1.2.0",
            "grid_template_areas": null,
            "object_position": null,
            "object_fit": null,
            "grid_auto_columns": null,
            "margin": null,
            "display": null,
            "left": null
          }
        },
        "8bc09170cf7d46788a6fa285c5917d68": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "DescriptionStyleModel",
          "state": {
            "_view_name": "StyleView",
            "_model_name": "DescriptionStyleModel",
            "description_width": "",
            "_view_module": "@jupyter-widgets/base",
            "_model_module_version": "1.5.0",
            "_view_count": null,
            "_view_module_version": "1.2.0",
            "_model_module": "@jupyter-widgets/controls"
          }
        },
        "e5c99098c0ac4ab383d0fdda2c9f560a": {
          "model_module": "@jupyter-widgets/base",
          "model_name": "LayoutModel",
          "state": {
            "_view_name": "LayoutView",
            "grid_template_rows": null,
            "right": null,
            "justify_content": null,
            "_view_module": "@jupyter-widgets/base",
            "overflow": null,
            "_model_module_version": "1.2.0",
            "_view_count": null,
            "flex_flow": null,
            "width": null,
            "min_width": null,
            "border": null,
            "align_items": null,
            "bottom": null,
            "_model_module": "@jupyter-widgets/base",
            "top": null,
            "grid_column": null,
            "overflow_y": null,
            "overflow_x": null,
            "grid_auto_flow": null,
            "grid_area": null,
            "grid_template_columns": null,
            "flex": null,
            "_model_name": "LayoutModel",
            "justify_items": null,
            "grid_row": null,
            "max_height": null,
            "align_content": null,
            "visibility": null,
            "align_self": null,
            "height": null,
            "min_height": null,
            "padding": null,
            "grid_auto_rows": null,
            "grid_gap": null,
            "max_width": null,
            "order": null,
            "_view_module_version": "1.2.0",
            "grid_template_areas": null,
            "object_position": null,
            "object_fit": null,
            "grid_auto_columns": null,
            "margin": null,
            "display": null,
            "left": null
          }
        }
      }
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/Ashraf-Al-Aodat/For-a-better-Intrusion-Detection-System-using-Deep-Learning-CIC2017-DB/blob/main/main.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "DprKNtoQOJhc",
        "outputId": "ed85a82b-0cec-4a42-f586-5b26916ac268"
      },
      "source": [
        "# download datasets..\n",
        "\n",
        "!wget http://205.174.165.80/CICDataset/CIC-IDS-2017/Dataset/GeneratedLabelledFlows.zip\n",
        "!unzip GeneratedLabelledFlows.zip\n",
        "!mv 'TrafficLabelling ' IDS\n",
        "\n",
        "!pip install swifter # install swifter for gpu pandas function processing\n",
        "\n",
        "\n",
        "print('Initializing..')\n",
        "\n",
        "import numpy as np\n",
        "from numpy import unique\n",
        "import pandas as pd\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.preprocessing import LabelEncoder, MinMaxScaler, StandardScaler\n",
        "from sklearn.metrics import mean_squared_error as mse, confusion_matrix as cm\n",
        "from sklearn.model_selection import train_test_split, StratifiedKFold\n",
        "from sklearn.metrics import accuracy_score\n",
        "import tensorflow as tf\n",
        "\n",
        "#from tensorflow.python.keras.utils import plot_model\n",
        "from tensorflow.python.keras.layers.merge import concatenate, Add\n",
        "from tensorflow.python.keras.layers import Input, Dense, Embedding, Dropout, Flatten, Conv2D, MaxPooling2D, UpSampling2D, Concatenate, Reshape, Lambda\n",
        "from tensorflow.python.keras import Model, Sequential\n",
        "from tensorflow.python.keras.layers.advanced_activations import LeakyReLU\n",
        "from keras.callbacks import LearningRateScheduler, ReduceLROnPlateau, EarlyStopping\n",
        "import tensorflow.python.keras.optimizers as opts\n",
        "import tensorflow.keras.optimizers as opts_v2\n",
        "from tensorflow.keras.utils import plot_model\n",
        "from tensorflow.keras import regularizers\n",
        "import math\n",
        "\n",
        "\n",
        "import seaborn as sns\n",
        "import sklearn.metrics\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "\n",
        "import sys\n",
        "import os\n",
        "import swifter\n",
        "\n",
        "import matplotlib.pyplot as plt\n",
        "import matplotlib as mpl\n",
        "\n",
        "print('Done')"
      ],
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "--2021-03-22 13:19:36--  http://205.174.165.80/CICDataset/CIC-IDS-2017/Dataset/GeneratedLabelledFlows.zip\n",
            "Connecting to 205.174.165.80:80... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 283876488 (271M) [application/zip]\n",
            "Saving to: ‘GeneratedLabelledFlows.zip.2’\n",
            "\n",
            "GeneratedLabelledFl 100%[===================>] 270.73M  5.42MB/s    in 82s     \n",
            "\n",
            "2021-03-22 13:20:57 (3.32 MB/s) - ‘GeneratedLabelledFlows.zip.2’ saved [283876488/283876488]\n",
            "\n",
            "Archive:  GeneratedLabelledFlows.zip\n",
            "   creating: TrafficLabelling /\n",
            "  inflating: TrafficLabelling /Wednesday-workingHours.pcap_ISCX.csv  \n",
            "  inflating: TrafficLabelling /Tuesday-WorkingHours.pcap_ISCX.csv  \n",
            "  inflating: TrafficLabelling /Thursday-WorkingHours-Morning-WebAttacks.pcap_ISCX.csv  \n",
            "  inflating: TrafficLabelling /Thursday-WorkingHours-Afternoon-Infilteration.pcap_ISCX.csv  \n",
            "  inflating: TrafficLabelling /Monday-WorkingHours.pcap_ISCX.csv  \n",
            "  inflating: TrafficLabelling /Friday-WorkingHours-Morning.pcap_ISCX.csv  \n",
            "  inflating: TrafficLabelling /Friday-WorkingHours-Afternoon-DDos.pcap_ISCX.csv  \n",
            "  inflating: TrafficLabelling /Friday-WorkingHours-Afternoon-PortScan.pcap_ISCX.csv  \n",
            "mv: cannot move 'TrafficLabelling ' to 'IDS/TrafficLabelling ': Directory not empty\n",
            "Requirement already satisfied: swifter in /usr/local/lib/python3.7/dist-packages (1.0.7)\n",
            "Requirement already satisfied: ipywidgets>=7.0.0cloudpickle>=0.2.2 in /usr/local/lib/python3.7/dist-packages (from swifter) (7.6.3)\n",
            "Requirement already satisfied: dask[dataframe]>=2.10.0 in /usr/local/lib/python3.7/dist-packages (from swifter) (2.12.0)\n",
            "Requirement already satisfied: psutil>=5.6.6 in /usr/local/lib/python3.7/dist-packages (from swifter) (5.8.0)\n",
            "Requirement already satisfied: bleach>=3.1.1 in /usr/local/lib/python3.7/dist-packages (from swifter) (3.3.0)\n",
            "Requirement already satisfied: tqdm>=4.33.0 in /usr/local/lib/python3.7/dist-packages (from swifter) (4.41.1)\n",
            "Requirement already satisfied: parso>0.4.0 in /usr/local/lib/python3.7/dist-packages (from swifter) (0.8.1)\n",
            "Requirement already satisfied: modin[ray]>=0.8.1.1 in /usr/local/lib/python3.7/dist-packages (from swifter) (0.9.1)\n",
            "Requirement already satisfied: pandas>=1.0.0 in /usr/local/lib/python3.7/dist-packages (from swifter) (1.1.5)\n",
            "Requirement already satisfied: widgetsnbextension~=3.5.0 in /usr/local/lib/python3.7/dist-packages (from ipywidgets>=7.0.0cloudpickle>=0.2.2->swifter) (3.5.1)\n",
            "Requirement already satisfied: nbformat>=4.2.0 in /usr/local/lib/python3.7/dist-packages (from ipywidgets>=7.0.0cloudpickle>=0.2.2->swifter) (5.1.2)\n",
            "Requirement already satisfied: traitlets>=4.3.1 in /usr/local/lib/python3.7/dist-packages (from ipywidgets>=7.0.0cloudpickle>=0.2.2->swifter) (5.0.5)\n",
            "Requirement already satisfied: ipykernel>=4.5.1 in /usr/local/lib/python3.7/dist-packages (from ipywidgets>=7.0.0cloudpickle>=0.2.2->swifter) (4.10.1)\n",
            "Requirement already satisfied: jupyterlab-widgets>=1.0.0; python_version >= \"3.6\" in /usr/local/lib/python3.7/dist-packages (from ipywidgets>=7.0.0cloudpickle>=0.2.2->swifter) (1.0.0)\n",
            "Requirement already satisfied: ipython>=4.0.0; python_version >= \"3.3\" in /usr/local/lib/python3.7/dist-packages (from ipywidgets>=7.0.0cloudpickle>=0.2.2->swifter) (5.5.0)\n",
            "Requirement already satisfied: numpy>=1.13.0; extra == \"dataframe\" in /usr/local/lib/python3.7/dist-packages (from dask[dataframe]>=2.10.0->swifter) (1.19.5)\n",
            "Requirement already satisfied: partd>=0.3.10; extra == \"dataframe\" in /usr/local/lib/python3.7/dist-packages (from dask[dataframe]>=2.10.0->swifter) (1.1.0)\n",
            "Requirement already satisfied: fsspec>=0.6.0; extra == \"dataframe\" in /usr/local/lib/python3.7/dist-packages (from dask[dataframe]>=2.10.0->swifter) (0.8.7)\n",
            "Requirement already satisfied: toolz>=0.7.3; extra == \"dataframe\" in /usr/local/lib/python3.7/dist-packages (from dask[dataframe]>=2.10.0->swifter) (0.11.1)\n",
            "Requirement already satisfied: six>=1.9.0 in /usr/local/lib/python3.7/dist-packages (from bleach>=3.1.1->swifter) (1.15.0)\n",
            "Requirement already satisfied: webencodings in /usr/local/lib/python3.7/dist-packages (from bleach>=3.1.1->swifter) (0.5.1)\n",
            "Requirement already satisfied: packaging in /usr/local/lib/python3.7/dist-packages (from bleach>=3.1.1->swifter) (20.9)\n",
            "Requirement already satisfied: ray<1.2.0,>=1.0.0; extra == \"ray\" in /usr/local/lib/python3.7/dist-packages (from modin[ray]>=0.8.1.1->swifter) (1.1.0)\n",
            "Requirement already satisfied: pyarrow==1.0; extra == \"ray\" in /usr/local/lib/python3.7/dist-packages (from modin[ray]>=0.8.1.1->swifter) (1.0.0)\n",
            "Requirement already satisfied: pytz>=2017.2 in /usr/local/lib/python3.7/dist-packages (from pandas>=1.0.0->swifter) (2018.9)\n",
            "Requirement already satisfied: python-dateutil>=2.7.3 in /usr/local/lib/python3.7/dist-packages (from pandas>=1.0.0->swifter) (2.8.1)\n",
            "Requirement already satisfied: notebook>=4.4.1 in /usr/local/lib/python3.7/dist-packages (from widgetsnbextension~=3.5.0->ipywidgets>=7.0.0cloudpickle>=0.2.2->swifter) (5.3.1)\n",
            "Requirement already satisfied: ipython-genutils in /usr/local/lib/python3.7/dist-packages (from nbformat>=4.2.0->ipywidgets>=7.0.0cloudpickle>=0.2.2->swifter) (0.2.0)\n",
            "Requirement already satisfied: jupyter-core in /usr/local/lib/python3.7/dist-packages (from nbformat>=4.2.0->ipywidgets>=7.0.0cloudpickle>=0.2.2->swifter) (4.7.1)\n",
            "Requirement already satisfied: jsonschema!=2.5.0,>=2.4 in /usr/local/lib/python3.7/dist-packages (from nbformat>=4.2.0->ipywidgets>=7.0.0cloudpickle>=0.2.2->swifter) (2.6.0)\n",
            "Requirement already satisfied: jupyter-client in /usr/local/lib/python3.7/dist-packages (from ipykernel>=4.5.1->ipywidgets>=7.0.0cloudpickle>=0.2.2->swifter) (5.3.5)\n",
            "Requirement already satisfied: tornado>=4.0 in /usr/local/lib/python3.7/dist-packages (from ipykernel>=4.5.1->ipywidgets>=7.0.0cloudpickle>=0.2.2->swifter) (5.1.1)\n",
            "Requirement already satisfied: decorator in /usr/local/lib/python3.7/dist-packages (from ipython>=4.0.0; python_version >= \"3.3\"->ipywidgets>=7.0.0cloudpickle>=0.2.2->swifter) (4.4.2)\n",
            "Requirement already satisfied: pygments in /usr/local/lib/python3.7/dist-packages (from ipython>=4.0.0; python_version >= \"3.3\"->ipywidgets>=7.0.0cloudpickle>=0.2.2->swifter) (2.6.1)\n",
            "Requirement already satisfied: pickleshare in /usr/local/lib/python3.7/dist-packages (from ipython>=4.0.0; python_version >= \"3.3\"->ipywidgets>=7.0.0cloudpickle>=0.2.2->swifter) (0.7.5)\n",
            "Requirement already satisfied: setuptools>=18.5 in /usr/local/lib/python3.7/dist-packages (from ipython>=4.0.0; python_version >= \"3.3\"->ipywidgets>=7.0.0cloudpickle>=0.2.2->swifter) (54.1.2)\n",
            "Requirement already satisfied: prompt-toolkit<2.0.0,>=1.0.4 in /usr/local/lib/python3.7/dist-packages (from ipython>=4.0.0; python_version >= \"3.3\"->ipywidgets>=7.0.0cloudpickle>=0.2.2->swifter) (1.0.18)\n",
            "Requirement already satisfied: pexpect; sys_platform != \"win32\" in /usr/local/lib/python3.7/dist-packages (from ipython>=4.0.0; python_version >= \"3.3\"->ipywidgets>=7.0.0cloudpickle>=0.2.2->swifter) (4.8.0)\n",
            "Requirement already satisfied: simplegeneric>0.8 in /usr/local/lib/python3.7/dist-packages (from ipython>=4.0.0; python_version >= \"3.3\"->ipywidgets>=7.0.0cloudpickle>=0.2.2->swifter) (0.8.1)\n",
            "Requirement already satisfied: locket in /usr/local/lib/python3.7/dist-packages (from partd>=0.3.10; extra == \"dataframe\"->dask[dataframe]>=2.10.0->swifter) (0.2.1)\n",
            "Requirement already satisfied: importlib-metadata; python_version < \"3.8\" in /usr/local/lib/python3.7/dist-packages (from fsspec>=0.6.0; extra == \"dataframe\"->dask[dataframe]>=2.10.0->swifter) (3.7.2)\n",
            "Requirement already satisfied: pyparsing>=2.0.2 in /usr/local/lib/python3.7/dist-packages (from packaging->bleach>=3.1.1->swifter) (2.4.7)\n",
            "Requirement already satisfied: redis>=3.5.0 in /usr/local/lib/python3.7/dist-packages (from ray<1.2.0,>=1.0.0; extra == \"ray\"->modin[ray]>=0.8.1.1->swifter) (3.5.3)\n",
            "Requirement already satisfied: grpcio>=1.28.1 in /usr/local/lib/python3.7/dist-packages (from ray<1.2.0,>=1.0.0; extra == \"ray\"->modin[ray]>=0.8.1.1->swifter) (1.32.0)\n",
            "Requirement already satisfied: aiohttp-cors in /usr/local/lib/python3.7/dist-packages (from ray<1.2.0,>=1.0.0; extra == \"ray\"->modin[ray]>=0.8.1.1->swifter) (0.7.0)\n",
            "Requirement already satisfied: aioredis in /usr/local/lib/python3.7/dist-packages (from ray<1.2.0,>=1.0.0; extra == \"ray\"->modin[ray]>=0.8.1.1->swifter) (1.3.1)\n",
            "Requirement already satisfied: prometheus-client>=0.7.1 in /usr/local/lib/python3.7/dist-packages (from ray<1.2.0,>=1.0.0; extra == \"ray\"->modin[ray]>=0.8.1.1->swifter) (0.9.0)\n",
            "Requirement already satisfied: protobuf>=3.8.0 in /usr/local/lib/python3.7/dist-packages (from ray<1.2.0,>=1.0.0; extra == \"ray\"->modin[ray]>=0.8.1.1->swifter) (3.12.4)\n",
            "Requirement already satisfied: pyyaml in /usr/local/lib/python3.7/dist-packages (from ray<1.2.0,>=1.0.0; extra == \"ray\"->modin[ray]>=0.8.1.1->swifter) (3.13)\n",
            "Requirement already satisfied: click>=7.0 in /usr/local/lib/python3.7/dist-packages (from ray<1.2.0,>=1.0.0; extra == \"ray\"->modin[ray]>=0.8.1.1->swifter) (7.1.2)\n",
            "Requirement already satisfied: py-spy>=0.2.0 in /usr/local/lib/python3.7/dist-packages (from ray<1.2.0,>=1.0.0; extra == \"ray\"->modin[ray]>=0.8.1.1->swifter) (0.3.5)\n",
            "Requirement already satisfied: colorful in /usr/local/lib/python3.7/dist-packages (from ray<1.2.0,>=1.0.0; extra == \"ray\"->modin[ray]>=0.8.1.1->swifter) (0.5.4)\n",
            "Requirement already satisfied: aiohttp in /usr/local/lib/python3.7/dist-packages (from ray<1.2.0,>=1.0.0; extra == \"ray\"->modin[ray]>=0.8.1.1->swifter) (3.7.4.post0)\n",
            "Requirement already satisfied: requests in /usr/local/lib/python3.7/dist-packages (from ray<1.2.0,>=1.0.0; extra == \"ray\"->modin[ray]>=0.8.1.1->swifter) (2.23.0)\n",
            "Requirement already satisfied: gpustat in /usr/local/lib/python3.7/dist-packages (from ray<1.2.0,>=1.0.0; extra == \"ray\"->modin[ray]>=0.8.1.1->swifter) (0.6.0)\n",
            "Requirement already satisfied: msgpack<2.0.0,>=1.0.0 in /usr/local/lib/python3.7/dist-packages (from ray<1.2.0,>=1.0.0; extra == \"ray\"->modin[ray]>=0.8.1.1->swifter) (1.0.2)\n",
            "Requirement already satisfied: opencensus in /usr/local/lib/python3.7/dist-packages (from ray<1.2.0,>=1.0.0; extra == \"ray\"->modin[ray]>=0.8.1.1->swifter) (0.7.12)\n",
            "Requirement already satisfied: colorama in /usr/local/lib/python3.7/dist-packages (from ray<1.2.0,>=1.0.0; extra == \"ray\"->modin[ray]>=0.8.1.1->swifter) (0.4.4)\n",
            "Requirement already satisfied: filelock in /usr/local/lib/python3.7/dist-packages (from ray<1.2.0,>=1.0.0; extra == \"ray\"->modin[ray]>=0.8.1.1->swifter) (3.0.12)\n",
            "Requirement already satisfied: jinja2 in /usr/local/lib/python3.7/dist-packages (from notebook>=4.4.1->widgetsnbextension~=3.5.0->ipywidgets>=7.0.0cloudpickle>=0.2.2->swifter) (2.11.3)\n",
            "Requirement already satisfied: Send2Trash in /usr/local/lib/python3.7/dist-packages (from notebook>=4.4.1->widgetsnbextension~=3.5.0->ipywidgets>=7.0.0cloudpickle>=0.2.2->swifter) (1.5.0)\n",
            "Requirement already satisfied: nbconvert in /usr/local/lib/python3.7/dist-packages (from notebook>=4.4.1->widgetsnbextension~=3.5.0->ipywidgets>=7.0.0cloudpickle>=0.2.2->swifter) (5.6.1)\n",
            "Requirement already satisfied: terminado>=0.8.1 in /usr/local/lib/python3.7/dist-packages (from notebook>=4.4.1->widgetsnbextension~=3.5.0->ipywidgets>=7.0.0cloudpickle>=0.2.2->swifter) (0.9.2)\n",
            "Requirement already satisfied: pyzmq>=13 in /usr/local/lib/python3.7/dist-packages (from jupyter-client->ipykernel>=4.5.1->ipywidgets>=7.0.0cloudpickle>=0.2.2->swifter) (22.0.3)\n",
            "Requirement already satisfied: wcwidth in /usr/local/lib/python3.7/dist-packages (from prompt-toolkit<2.0.0,>=1.0.4->ipython>=4.0.0; python_version >= \"3.3\"->ipywidgets>=7.0.0cloudpickle>=0.2.2->swifter) (0.2.5)\n",
            "Requirement already satisfied: ptyprocess>=0.5 in /usr/local/lib/python3.7/dist-packages (from pexpect; sys_platform != \"win32\"->ipython>=4.0.0; python_version >= \"3.3\"->ipywidgets>=7.0.0cloudpickle>=0.2.2->swifter) (0.7.0)\n",
            "Requirement already satisfied: zipp>=0.5 in /usr/local/lib/python3.7/dist-packages (from importlib-metadata; python_version < \"3.8\"->fsspec>=0.6.0; extra == \"dataframe\"->dask[dataframe]>=2.10.0->swifter) (3.4.1)\n",
            "Requirement already satisfied: typing-extensions>=3.6.4; python_version < \"3.8\" in /usr/local/lib/python3.7/dist-packages (from importlib-metadata; python_version < \"3.8\"->fsspec>=0.6.0; extra == \"dataframe\"->dask[dataframe]>=2.10.0->swifter) (3.7.4.3)\n",
            "Requirement already satisfied: async-timeout in /usr/local/lib/python3.7/dist-packages (from aioredis->ray<1.2.0,>=1.0.0; extra == \"ray\"->modin[ray]>=0.8.1.1->swifter) (3.0.1)\n",
            "Requirement already satisfied: hiredis in /usr/local/lib/python3.7/dist-packages (from aioredis->ray<1.2.0,>=1.0.0; extra == \"ray\"->modin[ray]>=0.8.1.1->swifter) (1.1.0)\n",
            "Requirement already satisfied: chardet<5.0,>=2.0 in /usr/local/lib/python3.7/dist-packages (from aiohttp->ray<1.2.0,>=1.0.0; extra == \"ray\"->modin[ray]>=0.8.1.1->swifter) (3.0.4)\n",
            "Requirement already satisfied: attrs>=17.3.0 in /usr/local/lib/python3.7/dist-packages (from aiohttp->ray<1.2.0,>=1.0.0; extra == \"ray\"->modin[ray]>=0.8.1.1->swifter) (20.3.0)\n",
            "Requirement already satisfied: yarl<2.0,>=1.0 in /usr/local/lib/python3.7/dist-packages (from aiohttp->ray<1.2.0,>=1.0.0; extra == \"ray\"->modin[ray]>=0.8.1.1->swifter) (1.6.3)\n",
            "Requirement already satisfied: multidict<7.0,>=4.5 in /usr/local/lib/python3.7/dist-packages (from aiohttp->ray<1.2.0,>=1.0.0; extra == \"ray\"->modin[ray]>=0.8.1.1->swifter) (5.1.0)\n",
            "Requirement already satisfied: idna<3,>=2.5 in /usr/local/lib/python3.7/dist-packages (from requests->ray<1.2.0,>=1.0.0; extra == \"ray\"->modin[ray]>=0.8.1.1->swifter) (2.10)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.7/dist-packages (from requests->ray<1.2.0,>=1.0.0; extra == \"ray\"->modin[ray]>=0.8.1.1->swifter) (2020.12.5)\n",
            "Requirement already satisfied: urllib3!=1.25.0,!=1.25.1,<1.26,>=1.21.1 in /usr/local/lib/python3.7/dist-packages (from requests->ray<1.2.0,>=1.0.0; extra == \"ray\"->modin[ray]>=0.8.1.1->swifter) (1.24.3)\n",
            "Requirement already satisfied: nvidia-ml-py3>=7.352.0 in /usr/local/lib/python3.7/dist-packages (from gpustat->ray<1.2.0,>=1.0.0; extra == \"ray\"->modin[ray]>=0.8.1.1->swifter) (7.352.0)\n",
            "Requirement already satisfied: blessings>=1.6 in /usr/local/lib/python3.7/dist-packages (from gpustat->ray<1.2.0,>=1.0.0; extra == \"ray\"->modin[ray]>=0.8.1.1->swifter) (1.7)\n",
            "Requirement already satisfied: opencensus-context==0.1.2 in /usr/local/lib/python3.7/dist-packages (from opencensus->ray<1.2.0,>=1.0.0; extra == \"ray\"->modin[ray]>=0.8.1.1->swifter) (0.1.2)\n",
            "Requirement already satisfied: google-api-core<2.0.0,>=1.0.0 in /usr/local/lib/python3.7/dist-packages (from opencensus->ray<1.2.0,>=1.0.0; extra == \"ray\"->modin[ray]>=0.8.1.1->swifter) (1.26.1)\n",
            "Requirement already satisfied: MarkupSafe>=0.23 in /usr/local/lib/python3.7/dist-packages (from jinja2->notebook>=4.4.1->widgetsnbextension~=3.5.0->ipywidgets>=7.0.0cloudpickle>=0.2.2->swifter) (1.1.1)\n",
            "Requirement already satisfied: testpath in /usr/local/lib/python3.7/dist-packages (from nbconvert->notebook>=4.4.1->widgetsnbextension~=3.5.0->ipywidgets>=7.0.0cloudpickle>=0.2.2->swifter) (0.4.4)\n",
            "Requirement already satisfied: pandocfilters>=1.4.1 in /usr/local/lib/python3.7/dist-packages (from nbconvert->notebook>=4.4.1->widgetsnbextension~=3.5.0->ipywidgets>=7.0.0cloudpickle>=0.2.2->swifter) (1.4.3)\n",
            "Requirement already satisfied: mistune<2,>=0.8.1 in /usr/local/lib/python3.7/dist-packages (from nbconvert->notebook>=4.4.1->widgetsnbextension~=3.5.0->ipywidgets>=7.0.0cloudpickle>=0.2.2->swifter) (0.8.4)\n",
            "Requirement already satisfied: defusedxml in /usr/local/lib/python3.7/dist-packages (from nbconvert->notebook>=4.4.1->widgetsnbextension~=3.5.0->ipywidgets>=7.0.0cloudpickle>=0.2.2->swifter) (0.7.1)\n",
            "Requirement already satisfied: entrypoints>=0.2.2 in /usr/local/lib/python3.7/dist-packages (from nbconvert->notebook>=4.4.1->widgetsnbextension~=3.5.0->ipywidgets>=7.0.0cloudpickle>=0.2.2->swifter) (0.3)\n",
            "Requirement already satisfied: googleapis-common-protos<2.0dev,>=1.6.0 in /usr/local/lib/python3.7/dist-packages (from google-api-core<2.0.0,>=1.0.0->opencensus->ray<1.2.0,>=1.0.0; extra == \"ray\"->modin[ray]>=0.8.1.1->swifter) (1.53.0)\n",
            "Requirement already satisfied: google-auth<2.0dev,>=1.21.1 in /usr/local/lib/python3.7/dist-packages (from google-api-core<2.0.0,>=1.0.0->opencensus->ray<1.2.0,>=1.0.0; extra == \"ray\"->modin[ray]>=0.8.1.1->swifter) (1.27.1)\n",
            "Requirement already satisfied: rsa<5,>=3.1.4; python_version >= \"3.6\" in /usr/local/lib/python3.7/dist-packages (from google-auth<2.0dev,>=1.21.1->google-api-core<2.0.0,>=1.0.0->opencensus->ray<1.2.0,>=1.0.0; extra == \"ray\"->modin[ray]>=0.8.1.1->swifter) (4.7.2)\n",
            "Requirement already satisfied: pyasn1-modules>=0.2.1 in /usr/local/lib/python3.7/dist-packages (from google-auth<2.0dev,>=1.21.1->google-api-core<2.0.0,>=1.0.0->opencensus->ray<1.2.0,>=1.0.0; extra == \"ray\"->modin[ray]>=0.8.1.1->swifter) (0.2.8)\n",
            "Requirement already satisfied: cachetools<5.0,>=2.0.0 in /usr/local/lib/python3.7/dist-packages (from google-auth<2.0dev,>=1.21.1->google-api-core<2.0.0,>=1.0.0->opencensus->ray<1.2.0,>=1.0.0; extra == \"ray\"->modin[ray]>=0.8.1.1->swifter) (4.2.1)\n",
            "Requirement already satisfied: pyasn1>=0.1.3 in /usr/local/lib/python3.7/dist-packages (from rsa<5,>=3.1.4; python_version >= \"3.6\"->google-auth<2.0dev,>=1.21.1->google-api-core<2.0.0,>=1.0.0->opencensus->ray<1.2.0,>=1.0.0; extra == \"ray\"->modin[ray]>=0.8.1.1->swifter) (0.4.8)\n",
            "Initializing..\n",
            "Done\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "yvlTaP9IOrQ_",
        "outputId": "4ab72f3b-4da2-44d5-c9fa-6669993379a0"
      },
      "source": [
        "directory = \"IDS\"\n",
        "files = os.listdir(directory)\n",
        "df = pd.DataFrame()\n",
        "for file in files:\n",
        "    print('concat : ', file)\n",
        "    if file.endswith('csv'):\n",
        "      df = df.append(pd.read_csv(directory + \"/\" + file, low_memory=False, encoding = 'ISO-8859-1'))\n",
        "      print('added')\n",
        "data_set = df\n",
        "df = None\n",
        "data_set.reset_index(inplace=True,drop=True);\n",
        "data_set.sample(frac=1).reset_index(drop=True);"
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "concat :  Thursday-WorkingHours-Morning-WebAttacks.pcap_ISCX.csv\n",
            "added\n",
            "concat :  Monday-WorkingHours.pcap_ISCX.csv\n",
            "added\n",
            "concat :  Friday-WorkingHours-Morning.pcap_ISCX.csv\n",
            "added\n",
            "concat :  Tuesday-WorkingHours.pcap_ISCX.csv\n",
            "added\n",
            "concat :  Friday-WorkingHours-Afternoon-DDos.pcap_ISCX.csv\n",
            "added\n",
            "concat :  Friday-WorkingHours-Afternoon-PortScan.pcap_ISCX.csv\n",
            "added\n",
            "concat :  Thursday-WorkingHours-Afternoon-Infilteration.pcap_ISCX.csv\n",
            "added\n",
            "concat :  Wednesday-workingHours.pcap_ISCX.csv\n",
            "added\n",
            "concat :  TrafficLabelling \n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 101,
          "referenced_widgets": [
            "cacc943016394d5a9addb3defd1c3170",
            "6a753e8f2f874049bd5b12eeef39861f",
            "946da29893d744d5a5d8b463d9a896b5",
            "b7d72c607bbb468686614ec7309aa8c5",
            "f35fc7f2ca904a3b89a561b80b842793",
            "63177b3db8724ac194f1d11902924a37",
            "8bc09170cf7d46788a6fa285c5917d68",
            "e5c99098c0ac4ab383d0fdda2c9f560a"
          ]
        },
        "id": "two9gX4kOuGz",
        "outputId": "f700441f-1c94-4901-85f9-7d02a36922b8"
      },
      "source": [
        "print('Cleaning data..')\n",
        "data_set = data_set.dropna()\n",
        "data_set.reset_index(inplace=True,drop=True);\n",
        "data_set.sample(frac=1).reset_index(drop=True);\n",
        "\n",
        "#drop_same = pd.DataFrame([data_set.min(), data_set.max()]);\n",
        "#drop_same.set_index(pd.Index(['min', 'max']), inplace=True)\n",
        "#display(drop_same)\n",
        "\n",
        "clean = data_set.drop([' Timestamp',' Bwd PSH Flags', ' Bwd URG Flags','Fwd Avg Bytes/Bulk', ' Fwd Avg Packets/Bulk', ' Fwd Avg Bulk Rate', ' Bwd Avg Bytes/Bulk', ' Bwd Avg Packets/Bulk', 'Bwd Avg Bulk Rate'], axis=1)\n",
        "data_set = None\n",
        "flow_Id = clean[clean.columns[0]]\n",
        "label = clean[clean.columns[len(clean.columns) - 1]]\n",
        "clean = clean.drop([clean.columns[0], clean.columns[len(clean.columns) - 1]], axis=1)\n",
        "categorical = clean.loc[:, clean.columns[0:5].tolist()]\n",
        "continuous = clean.drop(categorical.columns, axis=1).swifter.apply(pd.to_numeric, axis=1, errors='coerce')\n",
        "#continuous = clean.drop(categorical.columns, axis=1).apply(pd.to_numeric, axis=1, errors='coerce')\n",
        "clean = pd.concat([categorical, continuous, flow_Id, label], axis=1)\n",
        "with pd.option_context('mode.use_inf_as_na', True):\n",
        "  clean = clean.dropna()\n",
        "clean.reset_index(inplace=True,drop=True);\n",
        "clean.sample(frac=1).reset_index(drop=True);\n",
        "label = clean[clean.columns[len(clean.columns) - 1]]\n",
        "flow_Id = clean[clean.columns[len(clean.columns) - 2]]\n",
        "categorical = clean.loc[:, clean.columns[0:5].tolist()]\n",
        "continuous = clean.loc[:, clean.columns[5:(len(clean.columns) - 2)]]\n",
        "print('Done')"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Cleaning data..\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "cacc943016394d5a9addb3defd1c3170",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "HBox(children=(FloatProgress(value=0.0, description='Dask Apply', max=4.0, style=ProgressStyle(description_wid…"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "Done\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WeVSA0nnOxXb"
      },
      "source": [
        "# prepare target\n",
        "def prepare_targets(data, binary):\n",
        "  if binary: \n",
        "    data[data != 'BENIGN'] = 1\n",
        "    data[data == 'BENIGN'] = 0\n",
        "    return data\n",
        "  else:\n",
        "    label_encoder = LabelEncoder()\n",
        "    label_encoder.fit(data)\n",
        "    data = label_encoder.transform(data)\n",
        "    return pd.Series(data)\n",
        "\n",
        "# prepare categorical input data\n",
        "def prepare_categorical_inputs(data):\n",
        "\t# label encode each column\n",
        "\tfor index in range(data.shape[1]):\n",
        "\t\tlabel_encoder = LabelEncoder()\n",
        "\t\tlabel_encoder.fit(data.loc[:, data.columns[index]])\n",
        "\t\t# encode store\n",
        "\t\tdata[data.columns[index]] = label_encoder.transform(data.loc[:, data.columns[index]].values)\n",
        "\t\t#data_test[data_test.columns[index]] = label_encoder.transform(data_test.loc[:, data_test.columns[index]].values)\n",
        "\treturn data\n",
        "\n",
        "\n",
        "def prepare_continuous_inputs(data, sl):\n",
        "  #continuous values normalization\n",
        "  columns = data.columns\n",
        "  index = data.index[0]\n",
        "  if sl:\n",
        "    standard_scaler = StandardScaler()\n",
        "    standard_scaler.fit(data)\n",
        "    data = standard_scaler.transform(data)\n",
        "  else:\n",
        "    min_max_scaler = MinMaxScaler()\n",
        "    min_max_scaler.fit(data)\n",
        "    data = min_max_scaler.transform(data)\n",
        "\n",
        "  data = pd.DataFrame(data, columns=columns)\n",
        "  data.index += index\n",
        "  return data\n",
        "\n",
        "# prepare slice function for keras layers.\n",
        "def crop(dim=1, start=0,end=1):\n",
        "\tdef func(x):\n",
        "\t\tif dim == 0:\n",
        "\t\t\treturn x[start: end]\n",
        "\t\tif dim == 1:\n",
        "\t\t\treturn x[:, start: end]\n",
        "\t\tif dim == 2:\n",
        "\t\t\treturn x[:, :, start: end]\n",
        "\t\tif dim == 3:\n",
        "\t\t\treturn x[:, :, :, start: end]\n",
        "\t\tif dim == 4:\n",
        "\t\t\treturn x[:, :, :, :, start: end]\n",
        "\treturn Lambda(func)"
      ],
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qAjdb9OlO2Vb"
      },
      "source": [
        "# dataset spliting test and train.\n",
        "# without flow id\n",
        "y = prepare_targets(label.copy(), True).astype('float32')\n",
        "\n",
        "x = pd.concat([prepare_categorical_inputs(categorical).astype('float32'),\n",
        "               prepare_continuous_inputs(continuous, False).astype('float32')], axis=1)\n",
        "\n",
        "x_categorical_columns = categorical.columns\n",
        "\n",
        "x_continuous_columns = continuous.columns\n",
        "\n",
        "\n",
        "folds = []\n",
        "for train_index, test_index in StratifiedKFold(n_splits=5).split(x, y):\n",
        "  folds.append([train_index, test_index])"
      ],
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BxedCUpCO-dp"
      },
      "source": [
        "def plot_history(history, epochs=50):\n",
        "    acc = history.history['accuracy']\n",
        "    val_acc = history.history['val_accuracy']\n",
        "\n",
        "    loss=history.history['loss']\n",
        "    val_loss=history.history['val_loss']\n",
        "\n",
        "    epochs_range = range(epochs)\n",
        "\n",
        "    plt.figure(figsize=(12, 6))\n",
        "    plt.subplot(1, 2, 1)\n",
        "    plt.plot(acc, label='Training Accuracy')\n",
        "    plt.plot(val_acc, label='Validation Accuracy')\n",
        "    plt.legend(loc='lower right')\n",
        "    plt.title('Training and Validation Accuracy')\n",
        "\n",
        "    plt.subplot(1, 2, 2)\n",
        "    plt.plot(loss, label='Training Loss')\n",
        "    plt.plot(val_loss, label='Validation Loss')\n",
        "    plt.legend(loc='upper right')\n",
        "    plt.title('Training and Validation Loss')\n",
        "    plt.show()\n",
        "\n",
        "def show_con(model, x, y, classes, pred, num=True, fontsize=10, figsize= 10):\n",
        "\n",
        "  con_mat = tf.math.confusion_matrix(labels=y.astype(np.int64), predictions=pred).numpy()\n",
        "\n",
        "  figure = plt.figure(figsize=(figsize, figsize))\n",
        "\n",
        "  if num:\n",
        "    fmt = 'g'\n",
        "  else:\n",
        "    fmt = '.0%' \n",
        "    con_mat = np.around(con_mat.astype('float') / con_mat.sum(axis=1)[:, np.newaxis], decimals=2)\n",
        "\n",
        "  con_mat_df = pd.DataFrame(con_mat, index = classes, columns = classes)\n",
        "\n",
        "  sns.heatmap(con_mat_df, annot=True, fmt=fmt,cmap=plt.cm.Blues)\n",
        "  \n",
        "  fontsize = fontsize\n",
        "  plt.yticks(rotation=0, fontsize=fontsize, fontweight='bold')\n",
        "  plt.xticks(rotation=0, fontsize=fontsize, fontweight='bold')\n",
        "  plt.ylabel('True label', fontsize=fontsize, fontweight='bold')\n",
        "  plt.xlabel('Predicted label', fontsize=fontsize, fontweight='bold')\n",
        "  plt.show()\n",
        "\n",
        "def get_callbacks(patience_lr):\n",
        "    reduce_lr_loss = ReduceLROnPlateau(monitor='loss', factor=0.1, patience=patience_lr, verbose=1, min_delta=1e-5, mode='min')\n",
        "    learningRateScheduler = LearningRateScheduler(schedulerExp)\n",
        "    earlyStopping = EarlyStopping(monitor='val_accuracy', patience=5)\n",
        "    return [reduce_lr_loss, learningRateScheduler]#, earlyStopping]\n",
        "\n",
        "\n",
        "def schedulerExp(epoch, lr):\n",
        "    return lr * tf.math.exp(-0.1)\n",
        "\n",
        "def schedulerStep(epoch, lr):\n",
        "  return lr * tf.math.pow(0.5, tf.math.floor((1+epoch) / 10.0))\n",
        "\n",
        "# build classification model.\n",
        "\n",
        "def build_model(epochs=50):\n",
        "  input = Input((74,))\n",
        "  embedding_layers = []\n",
        "  for index in range(len(x_categorical_columns)):\n",
        "    labels_length = len(unique(x[x_categorical_columns[index]]))\n",
        "    dim = round(math.pow(labels_length, 1/4)) + 1\n",
        "    s = crop(1, index, index+1)(input)\n",
        "    embedding_layer = Embedding(output_dim=dim, input_dim=labels_length, input_length=1)(s)\n",
        "    embedding_layer = Reshape((dim,))(embedding_layer)\n",
        "    embedding_layers.append(embedding_layer)\n",
        "\n",
        "  embedding_layers.append(crop(1, 5, 74)(input))\n",
        "  em = Concatenate()(embedding_layers)\n",
        "\n",
        "  l = Dense(128, activation='relu')(em)\n",
        "  l = Dropout(0.25)(l)\n",
        "  l = Dense(64, activation='relu')(l)\n",
        "  l = Dropout(0.50)(l)\n",
        "  l = Dense(32, activation='relu')(l)\n",
        "  \n",
        "  \n",
        "  output = Dense(len(y.unique())-1, activation='sigmoid')(l)\n",
        "  \n",
        "  lr = 1e-3 \n",
        "\n",
        "  opt = tf.keras.optimizers.Adam(lr=lr, decay=lr / epochs)\n",
        "  loss = loss=tf.keras.losses.BinaryCrossentropy(from_logits=True)\n",
        "\n",
        "  model = tf.keras.models.Model(inputs=input, outputs=output)\n",
        "  model.compile(opt, loss, metrics=[\"accuracy\"])\n",
        "  \n",
        "  return model\n",
        "\n",
        "def fit_model(model, x_train_cv, y_train_cv, x_valid_cv, y_valid_cv, batch_size= 50, epochs= 50):\n",
        "  \n",
        "  callbacks = get_callbacks(patience_lr=5)\n",
        "  history = model.fit(x=x_train_cv, y=y_train_cv, steps_per_epoch=len(x_train_cv) / batch_size, epochs=epochs,\n",
        "                      batch_size=batch_size, shuffle=True, verbose=1,\n",
        "                      validation_data = (x_valid_cv, y_valid_cv), callbacks = callbacks)\n",
        "  return history\n"
      ],
      "execution_count": 97,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "B1jDFtCvPBvl",
        "outputId": "55ec2f91-8921-4653-9e49-328f92930e1f"
      },
      "source": [
        "model = build_model()\n",
        "model.summary()"
      ],
      "execution_count": 98,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model: \"model_8\"\n",
            "__________________________________________________________________________________________________\n",
            "Layer (type)                    Output Shape         Param #     Connected to                     \n",
            "==================================================================================================\n",
            "input_9 (InputLayer)            [(None, 74)]         0                                            \n",
            "__________________________________________________________________________________________________\n",
            "lambda_48 (Lambda)              (None, 1)            0           input_9[0][0]                    \n",
            "__________________________________________________________________________________________________\n",
            "lambda_49 (Lambda)              (None, 1)            0           input_9[0][0]                    \n",
            "__________________________________________________________________________________________________\n",
            "lambda_50 (Lambda)              (None, 1)            0           input_9[0][0]                    \n",
            "__________________________________________________________________________________________________\n",
            "lambda_51 (Lambda)              (None, 1)            0           input_9[0][0]                    \n",
            "__________________________________________________________________________________________________\n",
            "lambda_52 (Lambda)              (None, 1)            0           input_9[0][0]                    \n",
            "__________________________________________________________________________________________________\n",
            "embedding_40 (Embedding)        (None, 1, 12)        204024      lambda_48[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "embedding_41 (Embedding)        (None, 1, 17)        1098846     lambda_49[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "embedding_42 (Embedding)        (None, 1, 13)        248456      lambda_50[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "embedding_43 (Embedding)        (None, 1, 16)        860656      lambda_51[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "embedding_44 (Embedding)        (None, 1, 2)         6           lambda_52[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "reshape_40 (Reshape)            (None, 12)           0           embedding_40[0][0]               \n",
            "__________________________________________________________________________________________________\n",
            "reshape_41 (Reshape)            (None, 17)           0           embedding_41[0][0]               \n",
            "__________________________________________________________________________________________________\n",
            "reshape_42 (Reshape)            (None, 13)           0           embedding_42[0][0]               \n",
            "__________________________________________________________________________________________________\n",
            "reshape_43 (Reshape)            (None, 16)           0           embedding_43[0][0]               \n",
            "__________________________________________________________________________________________________\n",
            "reshape_44 (Reshape)            (None, 2)            0           embedding_44[0][0]               \n",
            "__________________________________________________________________________________________________\n",
            "lambda_53 (Lambda)              (None, 69)           0           input_9[0][0]                    \n",
            "__________________________________________________________________________________________________\n",
            "concatenate_8 (Concatenate)     (None, 129)          0           reshape_40[0][0]                 \n",
            "                                                                 reshape_41[0][0]                 \n",
            "                                                                 reshape_42[0][0]                 \n",
            "                                                                 reshape_43[0][0]                 \n",
            "                                                                 reshape_44[0][0]                 \n",
            "                                                                 lambda_53[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "dense_32 (Dense)                (None, 128)          16640       concatenate_8[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "dropout_16 (Dropout)            (None, 128)          0           dense_32[0][0]                   \n",
            "__________________________________________________________________________________________________\n",
            "dense_33 (Dense)                (None, 64)           8256        dropout_16[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "dropout_17 (Dropout)            (None, 64)           0           dense_33[0][0]                   \n",
            "__________________________________________________________________________________________________\n",
            "dense_34 (Dense)                (None, 32)           2080        dropout_17[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "dense_35 (Dense)                (None, 1)            33          dense_34[0][0]                   \n",
            "==================================================================================================\n",
            "Total params: 2,438,997\n",
            "Trainable params: 2,438,997\n",
            "Non-trainable params: 0\n",
            "__________________________________________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "yMtppUbSRoqB",
        "outputId": "9cdc54ef-1819-4407-9436-d547c395c471"
      },
      "source": [
        "#fold 5\n",
        "\n",
        "start = 1\n",
        "end = 5\n",
        "\n",
        "cv_sum = 0;\n",
        "\n",
        "cv_acc_list = []\n",
        "\n",
        "#with open('folds.npy', 'rb') as f:\n",
        "#  import numpy as np\n",
        "#  folds = np.load(f, allow_pickle=True)\n",
        "\n",
        "for i in range(start-1, end):\n",
        "  print('\\nFold ',i)\n",
        "  print('                   ')\n",
        "\n",
        "  x_train_cv = x.loc[folds[i][0]]\n",
        "  y_train_cv = y.loc[folds[i][0]]\n",
        "  x_test_cv = x.loc[folds[i][1]]\n",
        "  y_test_cv= y.loc[folds[i][1]]\n",
        "  model = build_model()\n",
        "\n",
        "  plot_model(model)\n",
        "  history = fit_model(model, x_train_cv, y_train_cv, x_test_cv, y_test_cv, batch_size=int(x_train_cv.shape[0] / 10))\n",
        "\n",
        "  pred = model.predict(x_test_cv)\n",
        "\n",
        "  acc = accuracy_score(y_test_cv, pred.round().astype(np.int32))\n",
        "  cv_acc_list.append(acc)\n",
        "  print('CV Evaluate:', model.evaluate(x_test_cv, y_test_cv))\n",
        "  cv_sum = cv_sum + acc \n",
        "\n",
        "  plot_history(history)\n",
        "  \n",
        "  show_con(model, x_test_cv, y_test_cv, ['Benign','Malicious'], pred.round(), True, 10, 7)\n",
        "  show_con(model, x_test_cv, y_test_cv, ['Benign','Malicious'], pred.round(), False, 10, 7)\n",
        "\n",
        "  with open('/content/drive/MyDrive/IDS_' + '_fold_'+ str(i) +'.npy', 'wb') as f:\n",
        "    import numpy as np  \n",
        "    np.save(f, history.history, allow_pickle=True)\n",
        "\n",
        "print('5 Fold Cv Avg: ', cv_sum / 5, 'List:', cv_acc_list)"
      ],
      "execution_count": 101,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "\n",
            "Fold  0\n",
            "                   \n",
            "Epoch 1/50\n",
            "10/10 [==============================] - 19s 2s/step - loss: 0.6436 - accuracy: 0.6564 - val_loss: 0.4892 - val_accuracy: 0.8032\n",
            "Epoch 2/50\n",
            "10/10 [==============================] - 17s 2s/step - loss: 0.4794 - accuracy: 0.8032 - val_loss: 0.4035 - val_accuracy: 0.8032\n",
            "Epoch 3/50\n",
            "10/10 [==============================] - 16s 2s/step - loss: 0.4024 - accuracy: 0.8116 - val_loss: 0.3364 - val_accuracy: 0.8505\n",
            "Epoch 4/50\n",
            "10/10 [==============================] - 17s 2s/step - loss: 0.3254 - accuracy: 0.8613 - val_loss: 0.2459 - val_accuracy: 0.9027\n",
            "Epoch 5/50\n",
            "10/10 [==============================] - 16s 2s/step - loss: 0.2272 - accuracy: 0.8929 - val_loss: 0.1366 - val_accuracy: 0.9572\n",
            "Epoch 6/50\n",
            "10/10 [==============================] - 16s 2s/step - loss: 0.1300 - accuracy: 0.9662 - val_loss: 0.0674 - val_accuracy: 0.9966\n",
            "Epoch 7/50\n",
            "10/10 [==============================] - 17s 2s/step - loss: 0.0695 - accuracy: 0.9937 - val_loss: 0.0341 - val_accuracy: 0.9984\n",
            "Epoch 8/50\n",
            "10/10 [==============================] - 17s 2s/step - loss: 0.0382 - accuracy: 0.9972 - val_loss: 0.0184 - val_accuracy: 0.9986\n",
            "Epoch 9/50\n",
            "10/10 [==============================] - 16s 2s/step - loss: 0.0227 - accuracy: 0.9980 - val_loss: 0.0109 - val_accuracy: 0.9989\n",
            "Epoch 10/50\n",
            "10/10 [==============================] - 17s 2s/step - loss: 0.0150 - accuracy: 0.9985 - val_loss: 0.0073 - val_accuracy: 0.9991\n",
            "Epoch 11/50\n",
            "10/10 [==============================] - 16s 2s/step - loss: 0.0110 - accuracy: 0.9988 - val_loss: 0.0053 - val_accuracy: 0.9993\n",
            "Epoch 12/50\n",
            "10/10 [==============================] - 17s 2s/step - loss: 0.0086 - accuracy: 0.9990 - val_loss: 0.0042 - val_accuracy: 0.9995\n",
            "Epoch 13/50\n",
            "10/10 [==============================] - 16s 2s/step - loss: 0.0073 - accuracy: 0.9991 - val_loss: 0.0035 - val_accuracy: 0.9995\n",
            "Epoch 14/50\n",
            "10/10 [==============================] - 16s 2s/step - loss: 0.0062 - accuracy: 0.9992 - val_loss: 0.0031 - val_accuracy: 0.9996\n",
            "Epoch 15/50\n",
            "10/10 [==============================] - 16s 2s/step - loss: 0.0056 - accuracy: 0.9992 - val_loss: 0.0027 - val_accuracy: 0.9996\n",
            "Epoch 16/50\n",
            "10/10 [==============================] - 16s 2s/step - loss: 0.0051 - accuracy: 0.9993 - val_loss: 0.0025 - val_accuracy: 0.9996\n",
            "Epoch 17/50\n",
            "10/10 [==============================] - 17s 2s/step - loss: 0.0046 - accuracy: 0.9994 - val_loss: 0.0023 - val_accuracy: 0.9996\n",
            "Epoch 18/50\n",
            "10/10 [==============================] - 17s 2s/step - loss: 0.0043 - accuracy: 0.9994 - val_loss: 0.0022 - val_accuracy: 0.9996\n",
            "Epoch 19/50\n",
            "10/10 [==============================] - 17s 2s/step - loss: 0.0042 - accuracy: 0.9994 - val_loss: 0.0021 - val_accuracy: 0.9996\n",
            "Epoch 20/50\n",
            "10/10 [==============================] - 17s 2s/step - loss: 0.0038 - accuracy: 0.9994 - val_loss: 0.0020 - val_accuracy: 0.9996\n",
            "Epoch 21/50\n",
            "10/10 [==============================] - 17s 2s/step - loss: 0.0037 - accuracy: 0.9995 - val_loss: 0.0019 - val_accuracy: 0.9997\n",
            "Epoch 22/50\n",
            "10/10 [==============================] - 16s 2s/step - loss: 0.0036 - accuracy: 0.9994 - val_loss: 0.0019 - val_accuracy: 0.9997\n",
            "Epoch 23/50\n",
            "10/10 [==============================] - 17s 2s/step - loss: 0.0034 - accuracy: 0.9995 - val_loss: 0.0018 - val_accuracy: 0.9997\n",
            "Epoch 24/50\n",
            "10/10 [==============================] - 17s 2s/step - loss: 0.0034 - accuracy: 0.9995 - val_loss: 0.0018 - val_accuracy: 0.9997\n",
            "Epoch 25/50\n",
            "10/10 [==============================] - 16s 2s/step - loss: 0.0032 - accuracy: 0.9995 - val_loss: 0.0017 - val_accuracy: 0.9997\n",
            "Epoch 26/50\n",
            "10/10 [==============================] - 16s 2s/step - loss: 0.0032 - accuracy: 0.9995 - val_loss: 0.0017 - val_accuracy: 0.9997\n",
            "Epoch 27/50\n",
            "10/10 [==============================] - 16s 2s/step - loss: 0.0031 - accuracy: 0.9995 - val_loss: 0.0017 - val_accuracy: 0.9997\n",
            "Epoch 28/50\n",
            "10/10 [==============================] - 16s 2s/step - loss: 0.0031 - accuracy: 0.9995 - val_loss: 0.0016 - val_accuracy: 0.9997\n",
            "Epoch 29/50\n",
            "10/10 [==============================] - 16s 2s/step - loss: 0.0029 - accuracy: 0.9995 - val_loss: 0.0016 - val_accuracy: 0.9997\n",
            "Epoch 30/50\n",
            "10/10 [==============================] - 16s 2s/step - loss: 0.0029 - accuracy: 0.9995 - val_loss: 0.0016 - val_accuracy: 0.9997\n",
            "Epoch 31/50\n",
            "10/10 [==============================] - 22s 2s/step - loss: 0.0028 - accuracy: 0.9995 - val_loss: 0.0016 - val_accuracy: 0.9997\n",
            "Epoch 32/50\n",
            "10/10 [==============================] - 17s 2s/step - loss: 0.0028 - accuracy: 0.9996 - val_loss: 0.0016 - val_accuracy: 0.9997\n",
            "Epoch 33/50\n",
            "10/10 [==============================] - 17s 2s/step - loss: 0.0028 - accuracy: 0.9996 - val_loss: 0.0016 - val_accuracy: 0.9997\n",
            "Epoch 34/50\n",
            "10/10 [==============================] - 16s 2s/step - loss: 0.0027 - accuracy: 0.9995 - val_loss: 0.0016 - val_accuracy: 0.9997\n",
            "Epoch 35/50\n",
            "10/10 [==============================] - 16s 2s/step - loss: 0.0027 - accuracy: 0.9996 - val_loss: 0.0015 - val_accuracy: 0.9997\n",
            "Epoch 36/50\n",
            "10/10 [==============================] - 16s 2s/step - loss: 0.0027 - accuracy: 0.9995 - val_loss: 0.0015 - val_accuracy: 0.9997\n",
            "Epoch 37/50\n",
            "10/10 [==============================] - 17s 2s/step - loss: 0.0027 - accuracy: 0.9995 - val_loss: 0.0015 - val_accuracy: 0.9997\n",
            "Epoch 38/50\n",
            "10/10 [==============================] - 17s 2s/step - loss: 0.0027 - accuracy: 0.9995 - val_loss: 0.0015 - val_accuracy: 0.9997\n",
            "Epoch 39/50\n",
            "10/10 [==============================] - 17s 2s/step - loss: 0.0026 - accuracy: 0.9996 - val_loss: 0.0015 - val_accuracy: 0.9997\n",
            "Epoch 40/50\n",
            "10/10 [==============================] - 16s 2s/step - loss: 0.0027 - accuracy: 0.9995 - val_loss: 0.0015 - val_accuracy: 0.9997\n",
            "Epoch 41/50\n",
            "10/10 [==============================] - 17s 2s/step - loss: 0.0026 - accuracy: 0.9996 - val_loss: 0.0015 - val_accuracy: 0.9997\n",
            "Epoch 42/50\n",
            "10/10 [==============================] - 17s 2s/step - loss: 0.0027 - accuracy: 0.9995 - val_loss: 0.0015 - val_accuracy: 0.9997\n",
            "Epoch 43/50\n",
            "10/10 [==============================] - 16s 2s/step - loss: 0.0027 - accuracy: 0.9995 - val_loss: 0.0015 - val_accuracy: 0.9997\n",
            "Epoch 44/50\n",
            "10/10 [==============================] - 17s 2s/step - loss: 0.0026 - accuracy: 0.9996 - val_loss: 0.0015 - val_accuracy: 0.9997\n",
            "Epoch 45/50\n",
            "10/10 [==============================] - 16s 2s/step - loss: 0.0025 - accuracy: 0.9996 - val_loss: 0.0015 - val_accuracy: 0.9997\n",
            "Epoch 46/50\n",
            "10/10 [==============================] - 17s 2s/step - loss: 0.0025 - accuracy: 0.9996 - val_loss: 0.0015 - val_accuracy: 0.9997\n",
            "Epoch 47/50\n",
            "10/10 [==============================] - 16s 2s/step - loss: 0.0026 - accuracy: 0.9996 - val_loss: 0.0015 - val_accuracy: 0.9997\n",
            "Epoch 48/50\n",
            "10/10 [==============================] - 17s 2s/step - loss: 0.0025 - accuracy: 0.9996 - val_loss: 0.0015 - val_accuracy: 0.9997\n",
            "Epoch 49/50\n",
            "10/10 [==============================] - 16s 2s/step - loss: 0.0026 - accuracy: 0.9996 - val_loss: 0.0015 - val_accuracy: 0.9997\n",
            "Epoch 50/50\n",
            "10/10 [==============================] - 16s 2s/step - loss: 0.0025 - accuracy: 0.9996 - val_loss: 0.0015 - val_accuracy: 0.9997\n",
            "17675/17675 [==============================] - 26s 1ms/step - loss: 0.0015 - accuracy: 0.9997\n",
            "CV Evaluate: [0.001477766316384077, 0.9996746778488159]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 864x432 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAeMAAAGpCAYAAAC+tF4yAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+WH4yJAAAgAElEQVR4nO3dd5wV1fn48c9Ds6CI3YgSbDHB3jFfjSX22KLG3v2J3dhLNIr1q4mJDSUh9ljQ2Fu+Rk1MjFGUqLEbUbFgFwTERjm/P2aAyzK7e8W7M7j7efua19577pmZc6/74tnnmXPPREoJSZJUnU5VD0CSpI7OYCxJUsUMxpIkVcxgLElSxQzGkiRVrEvVA2gP5ljlUKekqzSjnxhY9RDUgczehWjk8Rr57+XnTw1s6NiqZGYsSVLFzIwlSeUJc8AifiqSJFXMzFiSVJ5oN5d5G8pgLEkqj2XqQn4qkiRVzMxYklQey9SFDMaSpPJYpi7kpyJJUsXMjCVJ5bFMXchgLEkqj2XqQn4qkiRVzMxYklQey9SFDMaSpPJYpi7kpyJJUsXMjCVJ5bFMXchgLEkqj2XqQn4qkiRVzMxYklQey9SFDMaSpPJYpi7kpyJJUsXMjCVJ5TEzLmQwliSVp5PXjIv4J4okSRUzM5YklccydSGDsSSpPH61qZB/okiSVDEzY0lSeSxTFzIYS5LKY5m6kH+iSJJUMTNjSVJ5LFMXMhhLkspjmbqQwViSVB4z40J+KpIkVczMWJJUHsvUhQzGkqTyWKYu5KciSVLFzIwlSeWxTF3IYCxJKo9l6kJ+KpIkVczMWJJUHjPjQgZjSVJ5vGZcyD9RJEntVkR0joinIuLu/PkSETE0IoZHxI0R0S1vny1/Pjx/vU/NMU7M21+OiE1r2jfL24ZHxAk17YXnaInBWJJUnujUuK0+PwderHl+LnB+SmlpYDSwX96+HzA6bz8/70dE9AV2BpYDNgMuzQN8Z+ASYHOgL7BL3relczTLYCxJKk9E47ZWTxWLAT8BLsufB7AhcHPe5Wpg2/zxNvlz8td/nPffBhiSUvoypfQ6MBxYM9+Gp5ReSyl9BQwBtmnlHM0yGEuSvpUion9EDKvZ+jfpcgFwHDA5fz4/8ElKaWL+/G2gV/64F/AWQP76mLz/1PYm+zTX3tI5muUELklSeRo4mzqlNBgYXHiaiC2BD1JK/46I9Rt20jZiMJYklae82dT/A2wdEVsAswM9gAuBnhHRJc9cFwNG5v1HAosDb0dEF2Ae4OOa9ilq9ylq/7iFczTLMrUkqd1JKZ2YUlospdSHbALWX1NKuwF/A3bIu+0F3JE/vjN/Tv76X1NKKW/fOZ9tvQSwDPA48ASwTD5zult+jjvzfZo7R7PMjCVJpYnqv2d8PDAkIs4EngIuz9svB/4YEcOBUWTBlZTS8xFxE/ACMBE4JKU0CSAiDgXuAzoDV6SUnm/lHM2KLIjrm5hjlUP9EFWa0U8MrHoI6kBm70JDo2f3Ha5s2L+X42/ep/LI3iiWqSVJqphlaklSedpNLttYBmNJUmlmgWvGsyTL1JIkVczMWJJUGjPjYgZjSVJpDMbFLFNLklQxM2NJUmnMjIsZjCVJ5TEWF7JMLUlSxcyMJUmlsUxdzGAsSSqNwbiYZWpJkipmZixJKo2ZcTGDsSSpNAbjYpapJUmqmJmxJKk8JsaFDMaSpNJYpi5mmVqSpIqZGUuSSmNmXMxgLEkqjcG4mGVqSZIqZmYsSSqPiXEhg7EkqTSWqYtZppYkqWJmxpKk0pgZFzMYS5JKYzAuZplakqSKmRlLkkpjZlzMYCxJKo+xuJBlakmSKmZmLEkqjWXqYgZjSVJpDMbFLFNLklQxM2NJUmnMjIsZjCVJ5TEWFzIYS5JKY2ZczGvGkiRVzMxYklQaM+NiBmPNtE6dgkeuO453PhjD9j//HYNP2511V1uaMZ9+AUD/U/7IM/8dObX/an1789DVR7PniVdy2wNPs+L3enHRSTszd/fZmTRpMr+6/D5u/suTAAw6dVdW7dubIBj+5gfsf8ofGf/5Vxy++4bs/dO1mThxMh+N/pQDT7uWN98dXcn716zryy+/ZJ89d2PCV18xcdIkNt5kUw4+9HBOPO5onn/+Obp06cryK6zAL089na5du1Y93A7FYFxslgvGEdEHeD1/moCPgfuB/imlT2fymA8B6wELppQ++uajFMChu27Ay6+/z9zdZ5/a9osLbue2B56eoW+nTsGZP9+GBx57aWrbZ19MYL9fXsOrb37Idxach0euO477//UiYz79nOPOu5Vx47Ogfu7R23HQzutx3pX38/RLb/E/uz3M519MYP+frcNZP9+WPU64su3frL5VunXrxmVXXM2c3bszYcIE9t5jV9ZZ90dsseXWnH3ueQCccOzR3HbLn9hx510rHq00a18zfgrYDXgY2AU46Bsc6/T8GGMbMC4BvRbqyWbrLMeVt/2rrv4H77wetz/4Hz4cNW5q2/A3P+DVNz8E4N0Px/Dh6HEsMN9cAFMDMcDss3UlpQTAP4a9wudfTADg8WdG0Gvhng15P2pfIoI5u3cHYOLEiUycOBEiWPdH6xERRATLr7Ai77//fsUj7XimfP6N2NqTWTkYfwg8ADyRP+8REb0i4paIGB0R70TEORHRGSAiRkTE+Ii4MCJGRcQTEbFIvu8pwA35MSIifhMRn0TEYxFxW0SkiFg/P06KiFci4rqIGBMRf4mIOUt+77O8Xx+7PSddeDuTJ6fp2gccshWP33givzp6O7p1zQoviy44D1tvuBKD//Rws8dbfbnv0q1LF157a1rh4vcDdmfEA2ezbJ+FuXTI32fYZ+9t1+a+R15o0DtSezNp0iR23G4bNlj3h/Rb+4esuOJKU1+bMGECd991B/+zzroVjrCDigZu7cisHIw3AT4AzgbeAQYC1wIbAxcCdwLHAwfX7DMn0AO4B1gd2L/guFsBRwHPAtflx2tqaWAk8Gj++vZNO0RE/4gYFhHDJn70/Ey8vW+vzdddng9GjeOpF9+arv2Ui+9kpZ+ewTq7/5p55+nO0ftsBGSB++QL75ia3Ta1yAI9uPzMPTlgwLXT9TlgwLUsuclJvPT6e+ywyWrT7bPzFmuwat/enH/1gw1+d2ovOnfuzE233sFf/vp3nnv2GV555b9TXzv7jNNYbbXVWXW11SscoTTNrByMh5IFwrOARYFjya77zg2cChyQ96sNppPJytmX5M/7FBx3g/znaSmli4E7Cvq8m1I6DriqueOklAanlFZPKa3eZYHl6ntH7cTaKy/JluutwEv3nMY15+zD+mt8jyvO3JP3PsquAnw1YSLX3PEYqy/XB4BV+/bmmnP24aV7TuOnG63CBSfuxFbrrwjA3N1n59aLDmLAJXfx+LMjZjjX5MmJP933b7b98cpT2zZYa1mO329Tdjji93w1YWKbv199u/Xo0YM11lyLf/0zq8z87tKBjB49imOOP7HikXVMlqmLzXITuGp8lFJ6ICIeAU4CNiQrTPwHOKam35iax5+nlL6IiCn/Qndu4fjFaVpmVP6znuN0OKdcfCenXHwnAOuutgxH7Plj9j35GhZZoMfUgLz1BivywqvvAPCDLQdM3Xfwabvz54ef466HnqFrl87c+Jv9uf7uoTNM+lpy8QWmlqy3XG9F/jsiu7a30rKLMfCkndn60Ev5cPRMzedTBzBq1Ci6dOlCjx49+OKLL3js0X+xz377c+vNf+Jfj/yTwZdfRadOs3Iu0n61tyDaKLNyMF40InYGplzUGU4WeH+Ut40E1gFeYtp15Xr8DTgCOCUi+gLbNGzEHdyVZ+3FAvPOTQQ88/LbHHbWkBb7b7/Jqqyz6tLM17M7u2/dD8i+DvXsK+9w2el7MHf3OYiAZ/87ksPPvhGAs4/clu5zzsZ1v9oPgLfeG83Pjvh9274xfet89OEHnPyLE5g8eRKTJyc22XQz1lt/A1ZdsS/fWXRR9tx1JwA23GhjDjz40IpHK0E0dx2vKk2+2gTZDOhHycrSE4ELyLLkrmTXfU/NM+gRwAIppbkiYnWyAH11Smnv2q82kX1V6jxgX+B54FNgU2CVlNLTEZGA51NKy0fEDsCfyEraA5ob8xyrHDprfYhq10Y/MbDqIagDmb1LY6dKLX3Mnxv27+Xw8zZvN2n2LJcZp5RG0PI8uZ81s1+fmsfDao+RUlq/tm9EjMyPswjZZLCRwIt539r9bm5lLJKkr8EydbFZLhiXZGvgTOArYBhwbErpy2qHJEnqqDpkMG6aKUuSymFiXKxDBmNJUjUsUxdzbr8kSRUzM5YklcbEuJjBWJJUmk6djMZFLFNLklQxM2NJUmksUxczGEuSSuNs6mKWqSVJqpiZsSSpNCbGxQzGkqTSWKYuZplakqSKmRlLkkpjZlzMYCxJKo2xuJhlakmSKmZmLEkqjWXqYgZjSVJpjMXFLFNLklQxM2NJUmksUxczGEuSSmMsLmaZWpKkipkZS5JKY5m6mMFYklQaY3Exy9SSJFXMYCxJKk1ENGyr41yzR8TjEfGfiHg+Ik7L25eIiKERMTwiboyIbnn7bPnz4fnrfWqOdWLe/nJEbFrTvlneNjwiTqhpLzxHcwzGkqTSRDRuq8OXwIYppZWAlYHNIqIfcC5wfkppaWA0sF/efz9gdN5+ft6PiOgL7AwsB2wGXBoRnSOiM3AJsDnQF9gl70sL5yhkMJYktUsp82n+tGu+JWBD4Oa8/Wpg2/zxNvlz8td/HFkKvg0wJKX0ZUrpdWA4sGa+DU8pvZZS+goYAmyT79PcOQoZjCVJpWlkmToi+kfEsJqtf8H5OkfE08AHwP3Aq8AnKaWJeZe3gV75417AWwD562OA+Wvbm+zTXPv8LZyjkLOpJUmlaeRs6pTSYGBwK30mAStHRE/gNuD7jRtB45gZS5LavZTSJ8DfgLWBnhExJRldDBiZPx4JLA6Qvz4P8HFte5N9mmv/uIVzFDIYS5JKU/Js6gXzjJiImAPYGHiRLCjvkHfbC7gjf3xn/pz89b+mlFLevnM+23oJYBngceAJYJl85nQ3skled+b7NHeOQpapJUmlKXnRj+8AV+eznjsBN6WU7o6IF4AhEXEm8BRwed7/cuCPETEcGEUWXEkpPR8RNwEvABOBQ/LyNxFxKHAf0Bm4IqX0fH6s45s5RyGDsSSpXUopPQOsUtD+GtlM6KbtXwA/a+ZYZwFnFbTfC9xb7zmaYzCWJJXGtamLGYwlSaUxGBdzApckSRUzM5YklcbEuJjBWJJUGsvUxSxTS5JUMTNjSVJpTIyLGYwlSaWxTF3MYCxJKo2xuJjXjCVJqpiZsSSpNJ1MjQsZjCVJpTEWF7NMLUlSxcyMJUmlcTZ1MYOxJKk0nYzFhSxTS5JUMTNjSVJpLFMXMxhLkkpjLC5mmVqSpIqZGUuSShOYGhcxGEuSSuNs6mKWqSVJqpiZsSSpNM6mLmYwliSVxlhczDK1JEkVMzOWJJXGWygWMxhLkkpjLC5mmVqSpIqZGUuSSuNs6mLNBuOIWLWlHVNKTzZ+OJKk9sxYXKylzHgYkFp4vXODxyJJUofUUjC+hpaDsSRJX4uzqYs1G4xTSnuXOA5JUgdgKC7W6mzqiJg/Iv4UEaMjYqP88SFlDE6SpI6gnq82DQI2A3oAk4ERwIFtOCZJUjsVEQ3b2pN6gvHGwHk1z18Almib4UiS2rNO0bitPaknGI8HFs4fdwY2Aj5usxFJktTB1LPoxxDgKLKZ1Xfn+/y6LQclSWqf2lt5uVHqCcYnAuOAn+TP7wb+t81GJElqt4zFxVoNximlCRFxNnBL3vRySmlC2w5LkqSOo56vNm1INoP6P/n2ekSs37bDkiS1R86mLlZPmfoyYC7gOrLgvRVwObBUG45LktQOtbdZ0I1Sz2zqOYETUkp7ppR2J7uG7N2eJElqkHru2vRHYKeIeJEseO8I3FTC2CRJ7Ux7Ky83Sr13bQrgwZrH6wLHtuG4JEntkKG4mHdtkiSpYt61SZJUGm+hWKzViVgR0R04HFgBmD1vTiml7dtyYJKk9sdYXKzerzbtRFaynvIxWr6WJKlB6gnGGwEDgUPIgvLPgKfaclCSpPbJ2dTF6vme8VzAM2RZ8ZzAE8AxbTkoSVL7FNG4rT2pJzN+mywgv0q28lYAb7XloCRJ6kjqCcYHAp8CTwPnkF0v/kVbDkqS1D45m7pYPXdterDmab82HIskqZ0zFhdraTnMsS3sl1JK87TBeCRJ6nBayoxH4VeY6jL6iYFVD0EdyNNvfFL1ENSB9FuqZ0OP52zqYi2twNWnxHFIkjqAer7C0xH5uUiSVDHvSyxJKo1l6mIGY0lSaToZiwvVFYwjohvQF3g9pTSmbYckSWqvDMbFWr1mHBGrAK8Bw4A1IuLFiPhDm49MkqQOop7MeCDZClwBTAauBf5fWw5KktQ+ec24WD2zqVcCrqp5/g6wUJuMRpLUrnWKxm3tSb03ilgvf7wisAswoq0GJElSR1NPMP4VcFn++Ddk5eq922pAkqT2yyp1sXpuFHFFRLwGbJE33ZNS+nvbDkuS1B5516ZirQbjiOhNNpt6YG1bSunNthyYJEkdRT1l6hHMeMOIVOe+kiRN5RrMxeoJqPcyLRjPC6wFPNFmI5IktVtWqYvVc814y9rnEfH/gJ+22YgkSepg6rlmfFST/j8BVm6zEUmS2i0ncBWrp0x9XkHbDY0eiCSp/TMWF6snGO9T83gSMCKl9M82Go8kSR1Oi8E4IjoDhwDnp5TMhiVJ30h7W8ayUVqcZZ5SmkQ2k7p3OcORJLVnnSIatrUmIhaPiL9FxAsR8XxE/Dxvny8i7o+IV/Kf8+btEREXRcTwiHgmIlatOdZeef9XImKvmvbVIuLZfJ+LIr8TRnPnaPZzqeOz+wg4LSJuzk90UURcWMd+kiRVaSJwdEqpL9APOCQi+gInAA+mlJYBHsyfA2wOLJNv/YFBkAVW4FSyr/auCZxaE1wHAfvX7LdZ3t7cOQrVc8148/zndjVtCfh5HftKkjRVmRO4UkrvAu/mj8dFxItAL2AbYP2829XAQ8Dxefs1KaUEPBYRPSPiO3nf+1NKo7L3EPcDm0XEQ0CPlNJjefs1wLbAn1s4R6Fmg3G+HvVhwAZf471LktSsRl4zjoj+ZBnsFINTSoOb6dsHWAUYCiycB2qA94CF88e9gLdqdns7b2up/e2Cdlo4R6GWMuM+QPeU0j0tHUCSpCrkgbcw+NaKiLmAW4AjUkpjoyY9TymliGi65HND1XOO1srU60XE7M0c/JqZHpkkqUMKyp1OHRFdyQLxdSmlW/Pm9yPiOymld/My9Ad5+0hg8ZrdF8vbRjKt5Dyl/aG8fbGC/i2do1BrE7gOBK5ssl2V/5Qk6WvpFI3bWpPPbL4ceDGl9Nual+4EpsyI3gu4o6Z9z3xWdT9gTF5qvg/YJCLmzSdubQLcl782NiL65efas8mxis5RqLXM+Hrg6Vb6SJI0K/ofYA/g2YiYEst+AZwD3BQR+wFvADvmr90LbAEMBz4jX/QqpTQqIs5g2k2STp8ymQs4mCxJnYNs4taf8/bmzlGotWB8V0rpplb6SJJUlzIX/chXi2zujD8u6J/IFroqOtYVwBUF7cOA5QvaPy46R3NaCsZvAOPrPZAkSa0JF6cu1GwwTiktUeZAJEnqqOpZ9EOSpIZwbepiBmNJUmmsUherZ21qSZLUhsyMJUmlqeduSx2RwViSVBqvGRezTC1JUsXMjCVJpbFKXcxgLEkqTaeSbxTxbWGZWpKkipkZS5JKY5m6mMFYklQaZ1MXs0wtSVLFzIwlSaVx0Y9iBmNJUmmMxcUsU0uSVDEzY0lSaSxTFzMYS5JKYywuZplakqSKmRlLkkpjBljMYCxJKk1Ypy7kHymSJFXMzFiSVBrz4mIGY0lSafxqUzHL1JIkVczMWJJUGvPiYgZjSVJprFIXs0wtSVLFzIwlSaXxe8bFDMaSpNJYji1mMJYklcbMuJh/pEiSVDEzY0lSacyLixmMJUmlsUxdzDK1JEkVMzOWJJXGDLCYwViSVBrL1MX8I0WSpIqZGUuSSmNeXMxgLEkqjVXqYpapJUmqmJmxJKk0nSxUFzIYS5JKY5m6mGVqSZIqZmYsSSpNWKYuZDCWJJXGMnUxy9SSJFXMzFiSVBpnUxczGEuSSmOZuphlakmSKmZmLEkqjZlxMYOxJKk0frWpmGVqSZIqZmYsSSpNJxPjQgZjSVJpLFMXs0wtSVLFzIwlSaVxNnUxg7EkqTSWqYtZppYkqWJmxpKk0jibupjBWJJUGsvUxSxTS5JUMTNjtZlTTj6Rf/z9Ieabb35uveNuAAZdcjG33HwT8807HwCHHXEU6/5oPR791yNceP5vmDBhAl27duXIo49lrX5rVzl8zSIuO/8Mnn78EXr0nJezB90AwOMPP8ht1/2Bd98awannX8kS3/sBAJ+OHcPFZ5/A6/99kXU2+gl7Hnzs1OPcfPUgHnnwXsZ/Oo7Btz403TmG/uMBbr/uDxBB7yWW4aDjzwDgnw/cw51DrgBg6533ZZ2NflLCO27fnE1drM0y44joExEp335Z0375lPZW9h+Q99shf54i4rlW9rkq77d6Y96Fvolttt2OQb+/bIb2Pfbcm5tuvYObbr2DdX+0HgA9552Xiy4ZxC2338UZZ5/DSSceV/ZwNYtaZ6MtOeaMC6ZrW+y7S3L4yeey7PKrTNfetVs3tt/jAHbe7/AZjrPyWutw6gVXztD+3sg3ufumqzn5vD/wv78bwm4HHAnAp+PGcPv1l3HK+Vdw6vlXcvv1lzF+3NgGvrOOKRq4tSdllan3jsxcwI4zeYxdgGNa6TMo7/fqTJ5DDbTa6mvQY5556ur7gx/0ZaGFFgZg6aWX4csvvuSrr75qy+HpW+L7K6xC97l7TNe2aO8l+M5i352h72yzz8H3lluZrt26zfDa0t9fgZ7zLTBD+9//7w5+vOUOU8/Ro2dWtXn234+x3CprMtfc89B97h4st8qaPPPvRxvxlqQZlBGMXwOWBNYnC8RdgZEAEbF8RLwQEZ9FxCcRcW9E9GrmODcA5+X79YiI30XEO/m+1+Z9Dsr7LZX32z8iXomI8RHxeESsk7c3zbqfm5KpR8SyETE0Ij6PiNER8XAbfCYd2pDrr2OHn27FKSefyNgxY2Z4/YG/3McP+valW8E/qFKjvTfyTd4f+SZnHL0/px+5L88MywLu6I8/ZL4FFp7ab775F2L0xx9WNcx2o1NEw7b2pIxg/CIwFNg3324HPslf+wq4GjgcGAhsCgyo45gXAAcADwKHkQX86UTEhsBg4EPgKKA3cGdEzN/KsQ8G1gCOA04E3ijqFBH9I2JYRAy7/A+D6xiyAHbcaRfu/r/7uemWO1hwwYU479fnTPf68OGvcMH55/HLU0+vaITqaCZNmsR777zFiecO4qDjz+TKi85m/Kfjqh5Wu2WZulhZE7iuAC4CZgM2A36Tt88G7AqsWNN3hTqOtxXwAbBXSmlyM322yH+emlK6PyJ6A78A+rVy7FfI/j9vATwBXFjUKaU0mCzY88VEWrz+rWnmX2BamXC7HX7GYQcfOPX5+++9x5GHH8qZZ5/L4r17VzE8dUDzLbAQSy67HF26dGHBRRZlkV69ef+dt5h3/gV56dknp/Yb9fEHfH+FVSscqdqzsq4ZDwEmAW8D99e0n0QWiE8ANgEmALM3+NypyU/yscC0P0Z6Tu2c0kBgI7JAvA3wWEQs2+AxdVgffvjB1Md/feABll5mGQDGjh3LoQf15+dHHs0qq65W1fDUAa269npTg+64MZ/w3sg3WWiRXqywWj+ee3Io48eNZfy4sTz35FBWWK21v+XVKlPjQqVkximlsRGxLzAupTQ5Zqz1zw9sR3Y9uR53AfsAV0fEQ8B3U0qnNOlzL3A0cFpELAXsB4wGHgPmy/vsEBF9gKnXqSPiQGABYHi+rQgsDLxc59iUO/6Yoxj2xON88sloNt7wRxx0yGEMe+JxXn7pJSJg0UV78csBWTl6yPXX8uZbbzJ40CUMHnQJAIP+cAXzz9/aVQW1d5eeezIvPfMkn479hCP22JKf7t6f7nP34NpB5zFuzCf8dsCR9F7yexx75kUAHL33tnz+2XgmTpzAk4/+nWPPuohevZfkxssv5tGH7uOrL7/giD22ZL1Nt+Gnu+8/NeieeMBOdOrUmZ32O4y5emQTD7fZZV8GHLFP/ng/5pq7vgmJap6LfhSLlNqmwpoHudeBe1JKWzZ57TlgObJAdxOwOFk5+BBgREpp5YgYAJwK/CyldHM+wer5lNLyEdED+DWwNTAPcFtKabeIuArYC1gjpTQsIvYnu/a7KPACcGRK6Z8RMSfZtet+wN3AWsCSKaWIiH2Ak4HFgHFkWf3hLZTDLVOrVE+/8UnrnaQG6bdUz4ZGz6GvjmnYv5drLTVPu4nsbRaMOxKDscpkMFaZGh2MH3+tccF4zSXbTzB2BS5JUmnaTfRsMNemliSpYgZjSVJ5SpxNHRFXRMQHtUspR8R8EXF/viDU/RExb94eEXFRRAyPiGciYtWaffbK+78SEXvVtK8WEc/m+1wU+ezk5s7REoOxJKk00cD/6nAV2doWtU4AHkwpLUO2cNQJefvmwDL51p9seWUiYj6yycRrAWsCp9YE10HA/jX7bdbKOZplMJYktUsppX8Ao5o0b0O28iP5z21r2q9JmceAnhHxHbKVIe9PKY1KKY0mWytjs/y1Himlx1I2E/qaJscqOkezDMaSpNJENHKbtixxvvWvYwgLp5TezR+/R7aOBGTrTbxV0+/tvK2l9rcL2ls6R7OcTS1JKk0jZ1PXLks8k/u3ejvfb6rec5gZS5I6kvfzEjP5zylr9I4kW4BqisXytpbaFytob+kczTIYS5LKU/3a1HeSrdRI/vOOmvY981nV/YAxean5PmCTiJg3n7i1CXBf/trYiOiXz6Les8mxis7RLMvUkqTSlLk2dUTcAKwPLBARb5PNij4HuCki9iO7Re6Oefd7ye7WNxz4jOz+B6SURkXEGWQ3DwI4PaU0ZVLYwWQztucA/pxvtHCO5sfqcuxHQwYAAAntSURBVJjfnMthqkwuh6kyNXo5zKfeGNewfy9X+e7c7WZBLzNjSVJpZrxpn8BgLEkqkbG4mMFYklQeo3EhZ1NLklQxM2NJUmnKnE39bWIwliSVxglcxSxTS5JUMTNjSVJpTIyLGYwlSeUxGheyTC1JUsXMjCVJpXE2dTGDsSSpNM6mLmaZWpKkipkZS5JKY2JczGAsSSqP0biQZWpJkipmZixJKo2zqYsZjCVJpXE2dTHL1JIkVczMWJJUGhPjYgZjSVJ5jMaFLFNLklQxM2NJUmmcTV3MYCxJKo2zqYtZppYkqWJmxpKk0pgYFzMYS5LKYzQuZJlakqSKmRlLkkrjbOpiBmNJUmmcTV3MMrUkSRUzM5YklcbEuJjBWJJUHqNxIcvUkiRVzMxYklQaZ1MXMxhLkkrjbOpilqklSaqYmbEkqTQmxsUMxpKk0limLmaZWpKkipkZS5JKZGpcxGAsSSqNZepilqklSaqYmbEkqTQmxsUMxpKk0limLmaZWpKkipkZS5JK49rUxQzGkqTyGIsLWaaWJKliZsaSpNKYGBczGEuSSuNs6mKWqSVJqpiZsSSpNM6mLmYwliSVx1hcyDK1JEkVMzOWJJXGxLiYwViSVBpnUxczGEuSSuMErmJeM5YkqWJmxpKk0limLmZmLElSxQzGkiRVzDK1JKk0lqmLGYwlSaVxNnUxy9SSJFXMzFiSVBrL1MUMxpKk0hiLi1mmliSpYmbGkqTymBoXMhhLkkrjbOpilqklSaqYmbEkqTTOpi5mMJYklcZYXMwytSRJFTMzliSVx9S4kMFYklQaZ1MXs0wtSVLFzIwlSaVxNnWxSClVPQZ1UBHRP6U0uOpxqGPw902zMsvUqlL/qgegDsXfN82yDMaSJFXMYCxJUsUMxqqS1+9UJn/fNMtyApckSRUzM5YkqWIGY0mSKmYw1tcSEX0iIuXb5Ij4MCKuj4i5vsExH8qPt0Ajx6pvjya/V7+sab98Snsr+w/I++2QP08R8Vwr+1yV91u9Me9CmnkGY82sp4DdgIeBXYCDvsGxTs+PMbYB49K3396RmQvYcSaPsQtwTCt9BuX9Xp3Jc0gNYzDWzPoQeAB4In/eIyJ6RcQtETE6It6JiHMiojNARIyIiPERcWFEjIqIJyJikXzfU4Ab8mNERPwmIj6JiMci4rY8e1k/P06KiFci4rqIGBMRf4mIOUt+72o7rwFLAuuTBeKuwEiAiFg+Il6IiM/y3497I6JXM8e5ATgv369HRPwu/538LCKuzfsclPdbKu+3f/67NT4iHo+IdfL2pln3c1My9YhYNiKGRsTn+e/9w23wmagDMBhrZm0CfACcDbwDDASuBTYGLgTuBI4HDq7ZZ06gB3APsDqwf8FxtwKOAp4FrsuP19TSZP9AP5q/vv03fjeaVbwIDAX2zbfbgU/y174CrgYOJ/t92xQYUMcxLwAOAB4EDiML+NOJiA3Jvvr0IdnvX2/gzoiYv5VjHwysARwHnAi8Ucd4pBl4owjNrKHAyWQZzEnAscB6ZHcrPbWm38bAxfnjyWTZyMrA7kCfguNukP88LaX0QET0A3Zt0ufdlNJxEbEz2T/IRcfRt9cVwEXAbMBmwG/y9tnIfhdWrOm7Qh3H24rsD8e9UkqTm+mzRf7z1JTS/RHRG/gF0K+VY79C9ju/BVmV6MI6xiPNwMxYM+ujlNIDwFn58w3J/lH6D1kAnrKdUbPP5ymlL4CJ+fPOLRy/pQk7o/Kf9RxH3z5DgEnA28D9Ne0nkQXiE8gqMxOA2Rt87tTkJ/lYYFry0nNq55QGAhuRBeJtgMciYtkGj0kdgJmxZtaieWa6bv58ODAG+FHeNhJYB3iJadeV6/E34AjglIjoS/YPnDqQlNLYiNgXGJdSmhwz3nNvfmA7suvJ9bgL2Ae4OiIeAr6bUjqlSZ97gaOB0yJiKWA/YDTwGDBf3meHiOgDTL1OHREHAguQ/f4PJ/tjYWHg5TrHJgEGY828Vcgmv4wF7iMrU08kuz53KNk/lM8C13zN494F/JbsemFn4J9kpehPWtpJ7UtK6caC5rOAlciu015I9sdfPY4gy6K3JptfcFvB+f4aEf3Jrv3+FngBODKl9HFE3ALsQZaNf8W0SWYAX5IF+sWAccAlZL+z0tficpia5UTEUcAzwCJk/+h+DiyVUvqy0oFJUhsxGGuWk5cS1yTLQoYBx6aUnqp0UJLUhgzGkiRVzNnUkiRVzGAsSVLFDMaSJFXMYCwxw12DUr5+9pA6lkOs59jH5MfcO38+IiI+bWWfH+ZrIq88E+fbIT/fgILX1s9fG1jHcVq981Ej95M6Mr9nLE3vKeDXwA7ATsB4sgUgphMRnVNKk5q21+kwoFsrfX5ItqzoCODpmTyPpG8JM2Npeu+klG4gW3cbYC2Y7q5Tl0bEGGCFiFg7Ih6NiE8j4r8RscuUg+TZ8EcR8QIzrp98MdkND4iIbhHxvxHxRn7nn3/kd6j6dd73yjzT7BMRP4iI+yNibN7/yJrz7RoR70bEG0xb37tVEXFBZPek/jIiXouIA5p06RoR1+Tv/d6ImDffr9mxSPr6DMbS9LpGxILAtvnzN2temxNYlOw+uR8Ad5OtU3wWWQZ7bUSsHBErkQXT98hWJNuohfOdkG/Pk61c9iTZ6k/X5a//juyeu6OBO4C+wK/IbtTx24jYKiIWBi4nuxHHWWQ37KjXi2RrPh8DvA9ckt8kYYrvAW/l49kc+GVEdGluLF/jvJJqWKaWpjfl1pCQra/9iyav75VSGhMRPyFbs3g+sttITrEh024scH5K6fKIWJxpmXZTW5HdlGCnlNK4KY0R8TSwGzA0pTQkIpYDlslfrr35xsZkf1TPDlyRUhocEZOAy+p8v0uS/RFQe0/ovkz7I+TtlNJJEdGNrFy/PrBsC2O5q87zSqphMJamN5QsUxwFvNBkCc7xKaWm6yFfA/yx5vkI4Cf542jy8+tobjWe+4Dzap6/Byw1M+eLiO+TrcX8NNl73p5sTfB674RUNBZJM8FgLE3vo5TSg3X0e5QsYG9GdleqLsCWZJniQ3mfIyKiE9mNBJpzF7A6cGNE3AysmFI6gqwsDbB5RHwG3Ep279x1gAeBz8jK37eRBcUvgH0i4k3g8Pre6lRzAItTXE5fLCLOJrszUaf8vb3cwlicRS3NBK8ZSzMhpTSKLPgOB84hyyw/A0aklP5DdherRchmTv+9hUOdk2/LA5cCq+btdwL/JstWr08pTSS7neQjZCXvM4C5gWdTSu+TlZA7AceT3favnvfwEnA+2R2HDgX+XNDtv/nrOwL/B5zR0ljqOa+kGbk2tSRJFTMzliSpYgZjSZIqZjCWJKliBmNJkipmMJYkqWIGY0mSKmYwliSpYv8fUoIIocYn6ccAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 504x504 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 504x504 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "Fold  1\n",
            "                   \n",
            "Epoch 1/50\n",
            "10/10 [==============================] - 20s 2s/step - loss: 0.5940 - accuracy: 0.7776 - val_loss: 0.4478 - val_accuracy: 0.8032\n",
            "Epoch 2/50\n",
            "10/10 [==============================] - 17s 2s/step - loss: 0.4571 - accuracy: 0.8054 - val_loss: 0.3509 - val_accuracy: 0.8700\n",
            "Epoch 3/50\n",
            "10/10 [==============================] - 17s 2s/step - loss: 0.3811 - accuracy: 0.8381 - val_loss: 0.2690 - val_accuracy: 0.8846\n",
            "Epoch 4/50\n",
            "10/10 [==============================] - 17s 2s/step - loss: 0.2941 - accuracy: 0.8683 - val_loss: 0.1594 - val_accuracy: 0.8995\n",
            "Epoch 5/50\n",
            "10/10 [==============================] - 18s 2s/step - loss: 0.1867 - accuracy: 0.9137 - val_loss: 0.0766 - val_accuracy: 0.9939\n",
            "Epoch 6/50\n",
            "10/10 [==============================] - 17s 2s/step - loss: 0.1005 - accuracy: 0.9808 - val_loss: 0.0368 - val_accuracy: 0.9986\n",
            "Epoch 7/50\n",
            "10/10 [==============================] - 17s 2s/step - loss: 0.0532 - accuracy: 0.9951 - val_loss: 0.0186 - val_accuracy: 0.9990\n",
            "Epoch 8/50\n",
            "10/10 [==============================] - 17s 2s/step - loss: 0.0301 - accuracy: 0.9974 - val_loss: 0.0104 - val_accuracy: 0.9992\n",
            "Epoch 9/50\n",
            "10/10 [==============================] - 17s 2s/step - loss: 0.0188 - accuracy: 0.9983 - val_loss: 0.0060 - val_accuracy: 0.9994\n",
            "Epoch 10/50\n",
            "10/10 [==============================] - 17s 2s/step - loss: 0.0131 - accuracy: 0.9985 - val_loss: 0.0039 - val_accuracy: 0.9996\n",
            "Epoch 11/50\n",
            "10/10 [==============================] - 17s 2s/step - loss: 0.0099 - accuracy: 0.9988 - val_loss: 0.0029 - val_accuracy: 0.9997\n",
            "Epoch 12/50\n",
            "10/10 [==============================] - 17s 2s/step - loss: 0.0079 - accuracy: 0.9990 - val_loss: 0.0023 - val_accuracy: 0.9998\n",
            "Epoch 13/50\n",
            "10/10 [==============================] - 17s 2s/step - loss: 0.0067 - accuracy: 0.9991 - val_loss: 0.0019 - val_accuracy: 0.9998\n",
            "Epoch 14/50\n",
            "10/10 [==============================] - 20s 2s/step - loss: 0.0057 - accuracy: 0.9992 - val_loss: 0.0016 - val_accuracy: 0.9998\n",
            "Epoch 15/50\n",
            "10/10 [==============================] - 23s 2s/step - loss: 0.0051 - accuracy: 0.9993 - val_loss: 0.0014 - val_accuracy: 0.9998\n",
            "Epoch 16/50\n",
            "10/10 [==============================] - 17s 2s/step - loss: 0.0047 - accuracy: 0.9993 - val_loss: 0.0013 - val_accuracy: 0.9998\n",
            "Epoch 17/50\n",
            "10/10 [==============================] - 17s 2s/step - loss: 0.0043 - accuracy: 0.9993 - val_loss: 0.0012 - val_accuracy: 0.9998\n",
            "Epoch 18/50\n",
            "10/10 [==============================] - 17s 2s/step - loss: 0.0041 - accuracy: 0.9994 - val_loss: 0.0011 - val_accuracy: 0.9998\n",
            "Epoch 19/50\n",
            "10/10 [==============================] - 17s 2s/step - loss: 0.0038 - accuracy: 0.9994 - val_loss: 0.0011 - val_accuracy: 0.9998\n",
            "Epoch 20/50\n",
            "10/10 [==============================] - 17s 2s/step - loss: 0.0036 - accuracy: 0.9995 - val_loss: 0.0010 - val_accuracy: 0.9998\n",
            "Epoch 21/50\n",
            "10/10 [==============================] - 17s 2s/step - loss: 0.0034 - accuracy: 0.9995 - val_loss: 0.0010 - val_accuracy: 0.9998\n",
            "Epoch 22/50\n",
            "10/10 [==============================] - 17s 2s/step - loss: 0.0033 - accuracy: 0.9995 - val_loss: 9.7002e-04 - val_accuracy: 0.9998\n",
            "Epoch 23/50\n",
            "10/10 [==============================] - 17s 2s/step - loss: 0.0032 - accuracy: 0.9995 - val_loss: 9.4506e-04 - val_accuracy: 0.9998\n",
            "Epoch 24/50\n",
            "10/10 [==============================] - 17s 2s/step - loss: 0.0031 - accuracy: 0.9995 - val_loss: 9.1854e-04 - val_accuracy: 0.9998\n",
            "Epoch 25/50\n",
            "10/10 [==============================] - 17s 2s/step - loss: 0.0030 - accuracy: 0.9995 - val_loss: 9.0413e-04 - val_accuracy: 0.9998\n",
            "Epoch 26/50\n",
            "10/10 [==============================] - 17s 2s/step - loss: 0.0029 - accuracy: 0.9995 - val_loss: 8.8867e-04 - val_accuracy: 0.9998\n",
            "Epoch 27/50\n",
            "10/10 [==============================] - 17s 2s/step - loss: 0.0029 - accuracy: 0.9995 - val_loss: 8.7335e-04 - val_accuracy: 0.9998\n",
            "Epoch 28/50\n",
            "10/10 [==============================] - 17s 2s/step - loss: 0.0028 - accuracy: 0.9996 - val_loss: 8.5789e-04 - val_accuracy: 0.9998\n",
            "Epoch 29/50\n",
            "10/10 [==============================] - 17s 2s/step - loss: 0.0028 - accuracy: 0.9995 - val_loss: 8.4821e-04 - val_accuracy: 0.9998\n",
            "Epoch 30/50\n",
            "10/10 [==============================] - 17s 2s/step - loss: 0.0027 - accuracy: 0.9996 - val_loss: 8.3842e-04 - val_accuracy: 0.9998\n",
            "Epoch 31/50\n",
            "10/10 [==============================] - 18s 2s/step - loss: 0.0027 - accuracy: 0.9995 - val_loss: 8.2968e-04 - val_accuracy: 0.9998\n",
            "Epoch 32/50\n",
            "10/10 [==============================] - 17s 2s/step - loss: 0.0026 - accuracy: 0.9996 - val_loss: 8.2245e-04 - val_accuracy: 0.9998\n",
            "Epoch 33/50\n",
            "10/10 [==============================] - 17s 2s/step - loss: 0.0026 - accuracy: 0.9996 - val_loss: 8.1525e-04 - val_accuracy: 0.9998\n",
            "Epoch 34/50\n",
            "10/10 [==============================] - 17s 2s/step - loss: 0.0025 - accuracy: 0.9996 - val_loss: 8.0964e-04 - val_accuracy: 0.9998\n",
            "Epoch 35/50\n",
            "10/10 [==============================] - 17s 2s/step - loss: 0.0026 - accuracy: 0.9996 - val_loss: 8.0671e-04 - val_accuracy: 0.9998\n",
            "Epoch 36/50\n",
            "10/10 [==============================] - 17s 2s/step - loss: 0.0025 - accuracy: 0.9996 - val_loss: 8.0537e-04 - val_accuracy: 0.9998\n",
            "Epoch 37/50\n",
            "10/10 [==============================] - 17s 2s/step - loss: 0.0025 - accuracy: 0.9996 - val_loss: 8.1298e-04 - val_accuracy: 0.9998\n",
            "Epoch 38/50\n",
            "10/10 [==============================] - 17s 2s/step - loss: 0.0025 - accuracy: 0.9996 - val_loss: 8.0953e-04 - val_accuracy: 0.9998\n",
            "Epoch 39/50\n",
            "10/10 [==============================] - 17s 2s/step - loss: 0.0025 - accuracy: 0.9996 - val_loss: 8.0498e-04 - val_accuracy: 0.9998\n",
            "Epoch 40/50\n",
            "10/10 [==============================] - 17s 2s/step - loss: 0.0024 - accuracy: 0.9996 - val_loss: 8.0110e-04 - val_accuracy: 0.9998\n",
            "Epoch 41/50\n",
            "10/10 [==============================] - 17s 2s/step - loss: 0.0024 - accuracy: 0.9996 - val_loss: 7.9740e-04 - val_accuracy: 0.9998\n",
            "Epoch 42/50\n",
            "10/10 [==============================] - 17s 2s/step - loss: 0.0025 - accuracy: 0.9996 - val_loss: 7.9327e-04 - val_accuracy: 0.9998\n",
            "Epoch 43/50\n",
            "10/10 [==============================] - 17s 2s/step - loss: 0.0024 - accuracy: 0.9996 - val_loss: 7.8983e-04 - val_accuracy: 0.9998\n",
            "Epoch 44/50\n",
            "10/10 [==============================] - 17s 2s/step - loss: 0.0024 - accuracy: 0.9996 - val_loss: 7.8688e-04 - val_accuracy: 0.9998\n",
            "Epoch 45/50\n",
            "10/10 [==============================] - 17s 2s/step - loss: 0.0024 - accuracy: 0.9996 - val_loss: 7.8443e-04 - val_accuracy: 0.9998\n",
            "Epoch 46/50\n",
            "10/10 [==============================] - 17s 2s/step - loss: 0.0024 - accuracy: 0.9996 - val_loss: 7.8227e-04 - val_accuracy: 0.9998\n",
            "Epoch 47/50\n",
            "10/10 [==============================] - 17s 2s/step - loss: 0.0024 - accuracy: 0.9996 - val_loss: 7.7969e-04 - val_accuracy: 0.9998\n",
            "Epoch 48/50\n",
            "10/10 [==============================] - 17s 2s/step - loss: 0.0024 - accuracy: 0.9996 - val_loss: 7.7463e-04 - val_accuracy: 0.9998\n",
            "Epoch 49/50\n",
            "10/10 [==============================] - 17s 2s/step - loss: 0.0024 - accuracy: 0.9996 - val_loss: 7.7286e-04 - val_accuracy: 0.9998\n",
            "Epoch 50/50\n",
            "10/10 [==============================] - 17s 2s/step - loss: 0.0024 - accuracy: 0.9996 - val_loss: 7.7181e-04 - val_accuracy: 0.9998\n",
            "17675/17675 [==============================] - 36s 2ms/step - loss: 7.7181e-04 - accuracy: 0.9998\n",
            "CV Evaluate: [0.0007718111737631261, 0.9998090267181396]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 864x432 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 504x504 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAc4AAAGtCAYAAACfhpREAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+WH4yJAAAgAElEQVR4nO3de5xVZb348c8XkMCjqEnAUTBMyRNeThdTO6GiaeIFySOVlpVXNMXTTUn0pKLZzUtp3qKwNMxLdiwEjqaWaZ5U/KWJaBQaASoXFZEEFJjn98da4GaCmb1xZs3stT/vXus1s9Z+9rOfjdN85/tdz36eSCkhSZKq06WjByBJUj0xcEqSVAMDpyRJNTBwSpJUAwOnJEk1MHBKklQDA6ckqZQi4rqIWBgRT27g8YiIKyJiVkQ8ERHvr6ZfA6ckqax+Agxr4fGDgUH5MQq4pppODZySpFJKKd0PvNxCkxHADSnzELBlRPxra/12a6sBNrKe7xvt8ksqzOJpV3b0ENRAenQj2rK/tvx9ueLxq04myxTXGJ9SGl9DF9sCcyvO5+XXXmjpSQZOSVJdyoNkLYGyTRg4JUnFiU51h/A5YEDFef/8Wos61TuQJKlAk4DP5rNr9wKWpJRaLNOCGackqUjRprdMW3mpuAkYCvSOiHnAecAmACmla4GpwCHALGAZcFw1/Ro4JUnFKbBUm1I6upXHE3Barf1aqpUkqQZmnJKk4hRYqm0vBk5JUnE616zajVL/70CSpAKZcUqSimOpVpKkGliqlSSpsZhxSpKKY6lWkqQaWKqVJKmxmHFKkopjqVaSpBpYqpUkqbGYcUqSimOpVpKkGliqlSSpsZhxSpKKU4KM08ApSSpOl/q/x1n/oV+SpAKZcUqSimOpVpKkGpTg4yj1H/olSSqQGackqTiWaiVJqoGlWkmSGosZpySpOJZqJUmqQQlKtQZOSVJxSpBx1v87kCSpQGackqTiWKqVJKkGlmolSWosZpySpOJYqpUkqQaWaiVJaixmnJKk4pQg4zRwSpKKU4J7nPUf+iVJKpAZpySpOJZqJUmqgaVaSZIaixmnJKk4lmolSaqBpVpJkhqLGackqTBRgozTwClJKkwZAqelWkmSamDGKUkqTv0nnAZOSVJxLNVKktRgzDglSYUpQ8Zp4JQkFaYMgdNSrSRJNTDjlCQVpgwZp4FTklSc+o+blmolSaqFGackqTCWaiVJqkEZAqelWkmSamDGKUkqTBkyTgOnJKkwZQiclmolSaqBGackqTj1n3AaOCVJxbFUK0lSgzHjlCQVpgwZp4FTklSYMgROS7WSpNKKiGERMTMiZkXEWet5fLuI+G1EPBYRT0TEIa31aeCUJBUn2vBo7aUiugJXAQcDg4GjI2Jws2b/DdyaUnofcBRwdWv9WqqVJBWm4FLtHsCslNKz+WvfDIwAnqpok4Be+fdbAM+31qkZpySpLkXEqIh4tOIY1azJtsDcivN5+bVK5wPHRMQ8YCpwemuva8YpSSpMW2acKaXxwPi32M3RwE9SSpdGxIeAn0bELimlpg09wcApSSpMwaXa54ABFef982uVTgCGAaSU/hARPYDewMINdWqpVpJUVtOAQRGxfUR0J5v8M6lZmznARwAi4j1AD2BRS52acUqSClNkxplSWhURo4G7gK7AdSmlGRFxAfBoSmkS8BXghxHxJbKJQsemlFJL/Ro4JUnFKXj9g5TSVLJJP5XXzq34/ingw7X0aalWkqQamHFKkgpThiX3DJySpMKUIXBaqpUkqQZmnJKkwpQh4zRwSpKKU/9x08ApSSpOGTJO73FKklQDM05JUmHMOKXcted9mr/f+00e/fnZa69t1WtTJl8zmum/OpfJ14xmy817rn3s0jEjefJX5/HILWN577/1B2DQO/vw4I1jeOSWsey52/YAdO3ahSnXjqZnj02KfUOqWw8+cD+HH3oQhw07kAk/zDbOGDvmK4w8YjhXfO+yte3GX3s1v7n3no4aZsOKiDY7OkqnC5wRMTAiUn40RcSiiPhZRGz2Fvq8L++vd1uOVW/66R0PMeK0q9a5dsZxB3LfIzPZdcQF3PfITM447qMAHDRkMDts9w52GTGO0V+/iSvOPgqAE0cO4cyLb+OI06/hi5/9CACjPr43N02ZxvIVK4t9Q6pLq1ev5hsXXcDV1/6I2ydN4c6pk/nLzD/zth49uO32O5jx5HSWLl3KokULmf7EE+z/kQM6esiqQ50ucFZ4DPg08ADZfmmffwt9XZD38WobjEvr8eAfn+HlJcvWuXbY0N2YeMfDAEy842GG77dbdn3f3fjZ5EcAeGT6bLbYvCf9evdi5crV9OzRnZ49urNy1Wq22Kwnh+yzCzfmbaXWPDn9CQYMeCf9Bwxgk+7dGXbIoTxw/+94fcUKmpqaWLVqFV27dOHq71/BqaNb3a9Y7cCMs30tAu4h2xYGoFdEbBsRv4iIxRHxfER8KyK6AkTE7Ih4LSIuj4iXI2JaRPTLn3sucFPeR0TEpRHxSkQ8FBG359no0LyfFBF/jYgbI2JJRPw6IjYt+L2XQp+tN2f+i9nfKvNffJU+W28OwDZ9tmTe/MVr2z234BW26bMlP7j1fsaccBA/uvAzfGfCXYwdNYzvTPg1rWxUIK21cMEC+v1rv7Xnffr2ZcGCBWy11ds5auQR7DN0P+bMmUNTauI9g3fuwJE2sGjDo4N05slBH+XNjUSfB64EbgY+AFwG9AO+SrYp6ffzdpsCvYApwDHAScCFzfodDnwZ+D1wK/DN9bz2jsDtwB+Ag4AjgZ9WNoiIUcAogG79h9Ktt/8nbE1r8W/u/MUcdNLlALxrQG+27bMlM/82nwkXfpbum3Rl3NVTmDVng3vLShs0Zuw5a78//dRT+Nr54/jhD67hLzP/zF4f+jBHfvwTHTg61ZvOnHE+DBwIXARsA5wJ7AtsDpwHnJy3O7DiOU1kJd01N9sGrqff/fKv41JK3wd+tZ42L6SUxgA/2VA/KaXxKaXdU0q7GzTXb+FLS+nXuxcA/Xr3YtHLSwF4fuEr9O+31dp22/bdkucXvrLOc8edNpzzr57MqUcP5ce//D/OufyXnHPywcUNXnWpT9++zH9h/trzhQsW0Ldv37Xnv/3NPQzeeWeWLVvG3LlzuPiyy7n713exfPnyjhhuQ7JU275eTCndQxY4AfYnS87/RBYs1xyVGeXylNIKYFV+3rWF/lvKf17Ov1bTjzZgyu+mc8zwPQE4ZvieTL7vibXXP3XYHgDssetAXv3H8rUlXYAhH9iRFxYt4Zk5i9i0xyakpkRTU2LTHt2LfxOqKzvvsitz5sxm3ry5rHzjDe6cOoV999sfgJUrVzLxhus59vgTeX3F62t/8TY1rWblSiefFaUMgbMzl2q3iYijgL3z81nAEmCf/NpzwBDgz7x5H7QavwW+CJwbEYOBEW024gZ2/TePZe8PDKL3lpsx684LufDaqVzy47uZ+O3j+dzHPsScF17mmDHXAXDn72dw0JCdmTHpPJatWMnJ509cp6+zThzGZ76atZ3wPw/y44uOpVvXLnzhG7cU/r5UX7p168bYc87l86NOpKlpNR874kh23HEQALfcdCOHjziCnj178u6ddmLF8hUc+bHhDNl7H3r16tXBI1c9ic428SIiBgJ/q7j0Ktm9xpPJMsDvkWWfmwDTgfNSSvdExGygd0pps4jYnSyYXp9SOjYi7iMr874DeAm4BDgemAH8g+w+5vtSSo9HRAJmpJR2iYiRwM/Jyrrnb2jMPd83unP9I6rUFk+7sqOHoAbSo1vbTsPZ8Yz/bbPfl7MuObhD0s5Ol3GmlGbT8nypj2/geQMrvn+0so+U0tDKthHxXN5PP+Bysuz16bxt5fNua2UskqQalGHloE4XOAtyOPB14A3gUeDMlNLrHTskSVI9aMjA2TwDlSQVowQJZ2MGTklSxyhDqbYzfxxFkqROx4xTklSYEiScBk5JUnG6dKn/yGmpVpKkGphxSpIKY6lWkqQaOKtWkqQGY8YpSSpMCRJOA6ckqTiWaiVJajBmnJKkwpQh4zRwSpIKU4K4aalWkqRamHFKkgpjqVaSpBqUIG5aqpUkqRZmnJKkwliqlSSpBiWIm5ZqJUmqhRmnJKkwlmolSapBCeKmpVpJkmphxilJKoylWkmSalCCuGmpVpKkWphxSpIKY6lWkqQalCBuWqqVJKkWZpySpMJYqpUkqQYliJuWaiVJqoUZpySpMJZqJUmqQRkCp6VaSZJqYMYpSSpMCRJOA6ckqTiWaiVJajBmnJKkwpQg4TRwSpKKU4ZSrYFTklSYEsRN73FKklQLM05JUmG6lCDlNHBKkgpTgrhpqVaSpFqYcUqSClOGWbVmnJKkwnSJtjuqERHDImJmRMyKiLM20OYTEfFURMyIiJ+11qcZpySplCKiK3AVcCAwD5gWEZNSSk9VtBkEjAU+nFJaHBF9WuvXwClJKkzBpdo9gFkppWfz174ZGAE8VdHmJOCqlNJigJTSwtY6tVQrSSpMRFseMSoiHq04RjV7uW2BuRXn8/Jrld4NvDsiHoyIhyJiWGvvwYxTklSXUkrjgfFvsZtuwCBgKNAfuD8idk0pvdLSEyRJKkRQaKn2OWBAxXn//FqlecDDKaWVwN8i4i9kgXTahjq1VCtJKkzBs2qnAYMiYvuI6A4cBUxq1uaXZNkmEdGbrHT7bIvvocb3LElSXUgprQJGA3cBTwO3ppRmRMQFEXF43uwu4KWIeAr4LXBmSumllvq1VCtJKkzRCyCklKYCU5tdO7fi+wR8OT+qYuCUJBWmBAsHWaqVJKkWZpySpMK4rZgkSTUoQdy0VCtJUi3MOCVJhSnDtmIbDJwR8f6WnphS+mPbD0eSVGYliJstZpyPAqmFx7u28VgkSer0WgqcN9By4JQkqSalnlWbUjq2wHFIkhpA/YfNKmbVRsTWEfHziFgcEQfk359WxOAkSepsqvk4yjXAMKAX0ATMBk5pxzFJkkoqItrs6CjVBM4DgUsqzp8Ctm+f4UiSyqzgbcXa5z1U0eY1oG/+fVfgAKDFLVckSSqrahZAuJlsu5UETM6fc3F7DkqSVE6lXgChwlhgKXBofj4Z+Ga7jUiSVFoliJutB86U0sqI+Abwi/zSzJTSyvYdliRJnVM1H0fZn2wm7Z/y428RMbR9hyVJKqMyzKqtplT7I2Az4EayQDscmADs0I7jkiSVUEfOhm0r1cyq3RQ4K6X02ZTSMWT3PN1VRZLUkKrZHeWnwCcj4mmyQPsJ4NYCxiZJKpmyz6qt3B0lgHsrvt8bOLMdxyVJKqH6D5vujiJJUk3cHUWSVJhSbyu2RkT8C/BfwK5Aj/xySikd2Z4DkySVTwniZtUfR/kkWdl2zVu2hCtJakjVBM4DgCuB08gC6MeBx9pzUJKkcirDrNpqPse5GfAEWba5KTANOKM9ByVJKqeItjs6SjUZ5zyy4PkM2YpBAcxtz0FJktRZVRM4TwH+ATwOfIvs/ubZ7TkoSVI5NcSs2pTSvRWne7XjWCRJJVeCuNniknuvtvC8lFLaoh3GI0lSp9ZSxvkyfuykKounXdnRQ1AD2eqDozt6CGogyx9r299vZZhV29LKQQMLHIckqQFU81GOzq4M70GSpMK4r6YkqTClLtVKktTWutR/3KwucEZEd2Aw8LeU0pL2HZIkqazKEDhbvccZEe8DniXb2PqDEfF0RPyw3UcmSVInVE3GeSXZykEBNAETgRPbc1CSpHIqwz3OambV/jvwk4rz54E+7TIaSVKpdYm2OzpKtYu875t/vxtwNDC7vQYkSVJnVk3g/A7ZZtYAl5KVbI9trwFJksqrBJXaqhZ5vy4ingUOyS9NSSn9rn2HJUkqo4bYHSUitiObVXtl5bWU0pz2HJgkSZ1RNaXa2fzzYu+pyudKkrRWGdZ5rSb4TeXNwLkVsCcwrd1GJEkqrRJUaqu6x3lY5XlEnAgc0W4jkiSpE6vmHueXm7U/FHhvu41IklRaDTE5CLhkPdduauuBSJLKrwRxs6rAeVzF96uB2Sml37fTeCRJ6tRaDJwR0RU4DfhuSsksU5L0lpRhd5QWA2dKaXVEJGC7gsYjSSqxRrnH+SIwLiI+SLbAO0BKKX2h/YYlSVLnVE3gPDj/+p8V1xJg4JQk1aQECeeGA2e+Pu3pwH7FDUeSVGZlv8c5EPiXlNKUgsYiSVKn11qpdt+I6LG+B1JKN7TDeCRJJRbUf8rZWuA8JT8qBdk9TgOnJKkmZS/VAvwMeLyIgUiSVA9aC5x3pJRuLWQkkqTSK3vG+XfgtaIGIkkqvyjB51E2GDhTStsXORBJkupBNQsgSJLUJspeqpUkqU2VoFJLl44egCRJ9cSMU5JUmEbZHUWSpDZRhnuclmolSaUVEcMiYmZEzIqIs1pod2REpIjYvbU+zTglSYUpslIbEV2Bq4ADgXnAtIiYlFJ6qlm7zcm2yny4mn7NOCVJhelCtNlRhT2AWSmlZ1NKbwA3AyPW0+5C4NvAiuregyRJdSgiRkXEoxXHqGZNtgXmVpzPy69V9vF+YEAtW2haqpUkFaYtS7UppfHA+I0fS3QBLgOOreV5Bk5JUmEKnlX7HDCg4rx/fm2NzYFdgPvyNXT7AZMi4vCU0qMb6tRSrSSprKYBgyJi+4joDhwFTFrzYEppSUqpd0ppYEppIPAQ0GLQBDNOSVKBilwAIaW0KiJGA3cBXYHrUkozIuIC4NGU0qSWe1g/A6ckqTBFLxyUUpoKTG127dwNtB1aTZ+WaiVJqoEZpySpMK5VK0lSDUoQNy3VSpJUCzNOSVJhypCtGTglSYWJEtRqyxD8JUkqjBmnJKkw9Z9vGjglSQUqw8dRLNVKklQDM05JUmHqP980cEqSClSCSq2lWkmSamHGKUkqTBk+x2nglCQVpgxlTgOnJKkwZcg4yxD8JUkqjBmnJKkw9Z9vGjglSQWyVCtJUoMx45QkFaYM2ZqBU5JUGEu1kiQ1GDNOSVJh6j/fNHBKkgpUgkqtpVpJkmphxilJKkyXEhRrDZySpMJYqpUkqcGYcUqSChOWaiVJqp6lWkmSGowZpySpMM6qlSSpBpZqJUlqMGackqTClCHjNHBKkgpTho+jWKqVJKkGZpySpMJ0qf+E08ApSSqOpVpJkhqMGackqTDOqpUkqQaWaiVJajBmnJKkwjirVpKkGliqlSSpwRg41e4efOB+Dj/0IA4bdiATfjgegLFjvsLII4ZzxfcuW9tu/LVX85t77+moYaqOXHvep/n7vd/k0Z+fvfbaVr02ZfI1o5n+q3OZfM1otty859rHLh0zkid/dR6P3DKW9/5bfwAGvbMPD944hkduGcueu20PQNeuXZhy7Wh69tik2DfUQCLa7ugo7RY4I2JgRKT8+FrF9Qlrrrfy/PPzdiPz8xQRT7bynJ/k7XZvm3eht2r16tV846ILuPraH3H7pCncOXUyf5n5Z97Wowe33X4HM56cztKlS1m0aCHTn3iC/T9yQEcPWXXgp3c8xIjTrlrn2hnHHch9j8xk1xEXcN8jMznjuI8CcNCQweyw3TvYZcQ4Rn/9Jq44+ygAThw5hDMvvo0jTr+GL372IwCM+vje3DRlGstXrCz2DTWQaMOjoxSVcR4bmc2AT2xkH0cDZ7TS5pq83TMb+RpqY09Of4IBA95J/wED2KR7d4YdcigP3P87Xl+xgqamJlatWkXXLl24+vtXcOro0zt6uKoTD/7xGV5esmyda4cN3Y2JdzwMwMQ7Hmb4frtl1/fdjZ9NfgSAR6bPZovNe9Kvdy9WrlxNzx7d6dmjOytXrWaLzXpyyD67cGPeVtqQIgLns8C7gKFkQXMT4DmAiNglIp6KiGUR8UpETI2IbTfQz03AJfnzekXEtRHxfP7ciXmbz+ftdsjbnRQRf42I1yLikYgYkl9vns0+uSYDjoidIuLhiFgeEYsj4oF2+DdpGAsXLKDfv/Zbe96nb18WLFjAVlu9naNGHsE+Q/djzpw5NKUm3jN45w4cqepdn603Z/6LrwIw/8VX6bP15gBs02dL5s1fvLbdcwteYZs+W/KDW+9nzAkH8aMLP8N3JtzF2FHD+M6EX5NSi8UwvUVdItrs6ChFzKp9GlgEHA9sD/wS2AXYFngDuB54CRgIjAXOB05qpc/vAccBE4H7gHc2bxAR+wPjgT+QBdxxwKSIGNRK36cCHwS+AKwEhqyvUUSMAkYBXHn1DzjhpFGtdKtKY8aes/b70089ha+dP44f/uAa/jLzz+z1oQ9z5Mc3tjAhZVqLf3PnL+agky4H4F0DerNtny2Z+bf5TLjws3TfpCvjrp7CrDkLCxhpY6n/ObXFfRzlOuAK4G3AMODS/PrbgE8Bu1W03bWK/oYDC4HPpZSaNtDmkPzreSmluyNiO+BsYK9W+v4r2X/bQ4BpwOXra5RSGk8WmFmxCv9E3YA+ffsy/4X5a88XLlhA3759157/9jf3MHjnnVm2bBlz587h4ssu55STTuCQw4bTs2fP9XUprdfCl5bSr3cv5r/4Kv1692LRy0sBeH7hK/Tvt9Xadtv23ZLnF76yznPHnTac86+ezKlHD+XHv/w/5jz/EuNGH85x51xf6HtQfSjqHufNwGpgHnB3xfVzyILmWcBHyTK8Hm382qnZV/KxwJt/OGy5tnFKVwIHkAXNEcBDEbFTG4+pYey8y67MmTObefPmsvKNN7hz6hT23W9/AFauXMnEG67n2ONP5PUVrxN56aWpaTUrVzo5Q7WZ8rvpHDN8TwCOGb4nk+97Yu31Tx22BwB77DqQV/+xfG1JF2DIB3bkhUVLeGbOIjbtsQmpKdHUlNi0R/fi30QjKMHsoEIyzpTSqxFxPLA0pdQU/1yb3hr4T7L7n9W4g6xUe31E3Ae8M6V0brM2U4GvAOMiYgfgBGAx8BDw9rzNyIgYSFY2BiAiTgF6A7PyYzegLzCzyrGpQrdu3Rh7zrl8ftSJNDWt5mNHHMmOO2bV8ltuupHDRxxBz549efdOO7Fi+QqO/Nhwhuy9D7169ergkaszu/6bx7L3BwbRe8vNmHXnhVx47VQu+fHdTPz28XzuYx9izgsvc8yY6wC48/czOGjIzsyYdB7LVqzk5PMnrtPXWScO4zNfzdpO+J8H+fFFx9Ktaxe+8I1bCn9fjaAMCyBEe90IzwPS34ApKaXDmj32JLAzWVC6FRhAVhI9DZidUnpvRJwPnAd8PKV0Wz55Z0ZKaZeI6AVcDBwObAHcnlL6dET8BPgc8MGU0qMRcRIwBtgGeAr4Ukrp9xGxKdm91r2AycCewLtSShERxwH/DfQHlpJly//VQknYUq0KtdUHR3f0ENRAlj92ZZtGuoefWdJmvy/33GGLDonC7RY4G4mBU0UycKpIbR04H3m27QLnHu/qmMDpWrWSpMLUf6HWJfckSaqJGackqTglSDkNnJKkwpRhVq2lWkmSamDGKUkqTEduB9ZWDJySpMKUIG5aqpUkqRZmnJKk4pQg5TRwSpIK46xaSZI6sYgYFhEzI2JWRJy1nse/HBFPRcQTEXFvRPzT/s7NGTglSYWJaLuj9deKrsBVwMHAYODoiBjcrNljwO4ppd2A24DvtNavgVOSVJiCt+PcA5iVUno2pfQG2W5XIyobpJR+m1Jalp8+RLYzVosMnJKk4rRh5IyIURHxaMUxqtmrbQvMrTifR8X+y+txAvC/rb0FJwdJkupSSmk8ML4t+oqIY4DdgX1ba2vglCQVpuBZtc8BAyrO++fX1hERBwDnAPumlF5vrVMDpySpMAUvuTcNGBQR25MFzKOAT607nngf8ANgWEppYTWdeo9TklRKKaVVwGjgLuBp4NaU0oyIuCAiDs+bXQxsBvw8Ih6PiEmt9WvGKUkqTNHLH6SUpgJTm107t+L7A2rt08ApSSpO/S8cZKlWkqRamHFKkgpThrVqDZySpMKUYSNrS7WSJNXAjFOSVJgSJJwGTklSgUoQOS3VSpJUAzNOSVJhnFUrSVINnFUrSVKDMeOUJBWmBAmngVOSVKASRE5LtZIk1cCMU5JUGGfVSpJUA2fVSpLUYMw4JUmFKUHCaeCUJBWoBJHTUq0kSTUw45QkFcZZtZIk1cBZtZIkNRgzTklSYUqQcBo4JUkFKkHktFQrSVINzDglSYVxVq0kSTVwVq0kSQ3GjFOSVJgSJJwGTklScSzVSpLUYMw4JUkFqv+U08ApSSqMpVpJkhqMGackqTAlSDgNnJKk4liqlSSpwZhxSpIK41q1kiTVov7jpqVaSZJqYcYpSSpMCRJOA6ckqTjOqpUkqcGYcUqSCuOsWkmSalH/cdNSrSRJtTDjlCQVpgQJp4FTklScMsyqNXBKkgpThslB3uOUJKkGZpySpMKUoVRrxilJUg0MnJIk1cBSrSSpMGUo1Ro4JUmFcVatJEkNxoxTklQYS7WSJNWgBHHTUq0kSbUw45QkFacEKaeBU5JUGGfVSpLUYMw4JUmFcVatJEk1KEHctFQrSVItzDglScUpQcppxilJKky04f+qer2IYRExMyJmRcRZ63n8bRFxS/74wxExsLU+DZySpFKKiK7AVcDBwGDg6IgY3KzZCcDilNKOwHeBb7fWr4FTklSYiLY7qrAHMCul9GxK6Q3gZmBEszYjgOvz728DPhLRcu/e42wDPbqVoWpfvIgYlVIa39HjqDfLH7uyo4dQl/x56xza8vdlRIwCRlVcGt/sv/G2wNyK83nAns26WdsmpbQqIpYAWwMvbuh1zTjVkUa13kRqM/68lUxKaXxKafeKo5A/jAyckqSyeg4YUHHeP7+23jYR0Q3YAnippU4NnJKkspoGDIqI7SOiO3AUMKlZm0nA5/LvRwK/SSmlljr1Hqc6kvebVCR/3hpMfs9yNHAX0BW4LqU0IyIuAB5NKU0CJgA/jYhZwMtkwbVF0UpglSRJFSzVSpJUAwOnJEk1MHCqJhExMCJSfjRFxKKI+FlEbPYW+rwv7693W45V9aPZz9XXKq5PWHO9leefn7cbmZ+niHiylef8JG+3e9u8CzUKA6c21mPAp4EHgKOBz7+Fvi7I+3i1Dcal+ndsZDYDPrGRfRwNnJqllx8AAAbLSURBVNFKm2vyds9s5GuoQRk4tbEWAfeQTfcG6BUR20bELyJicUQ8HxHfyteKJCJmR8RrEXF5RLwcEdMiol/+3HOBm/I+IiIujYhXIuKhiLg9zwqG5v2kiPhrRNwYEUsi4tcRsWnB713t51ngXcBQsqC5Cfnn7iJil4h4KiKW5T8fUyNi2w30cxNwSf68XhFxbf4zuSwiJuZtPp+32yFvd1L+s/VaRDwSEUPy682z2SfXZMARsVO+MPjy/Of+gXb4N1EnY+DUxvoosBD4BvA8cCUwETgQuJzss1FfBU6teM6mQC9gCrA7cNJ6+h0OfBmYDtyY99fcjmS/TP+QP37kW3436iyeBh4Gjs+PXwKv5I+9Qbam6H+R/bwdBJxfRZ/fA04G7gVOJwvO64iI/ck+rrKI7OdvO2BSRGzdSt+nAh8ExgBjgb9XMR7VOT/HqY31MPDfZJnBOcCZwL5ku+2dV9HuQOD7+fdNZH/lvxc4Bhi4nn73y7+OSyndExF7AZ9q1uaFlNKYiDiK7Jfn+vpR/boOuAJ4GzAMuDS//jayn4XdKtruWkV/w8n+yPtcSqlpA20Oyb+el1K6OyK2A84G9mql77+S/cwfQlZ9ubyK8ajOmXFqY72YUroHuCg/35/sF8ifyILlmuPCiucsTymtAFbl511b6L+lySAv51+r6Uf152ZgNdmC3HdXXD+HLGieRVbxWAn0aOPXTs2+ko8F3kw0tlzbOKUrgQPIguYI4KGI2KmNx6ROxoxTG2ubPOPbOz+fBSwB9smvPQcMAf7Mm/dBq/Fb4IvAufm+ec23AFLJpZRejYjjgaUppab17PC0NfCfZPc/q3EHcBxwfUTcB7wzpXRuszZTga8A4yJiB/I9GoGHgLfnbUbmmxyvva8aEacAvcl+/meRBfa+wMwqx6Y6ZODUxnof2cSKV8mWszqTLAP8HjCa7JfadOCGGvu9A7iM7P5WV+D3ZOXYV1p6ksolpXTLei5fBPw72X3Fy8n+UKvGF8my08PJ7offvp7X+02+RdUYsp+/p4AvpZReiohfAJ8hy3Lf4M0JTACvkwXl/sBSsk2Tf1/luFSnXHJPnU5EfBl4AuhH9gtyObBDSun1Dh2YJGHgVCeUl9P2IPvr/lHgzJTSYx06KEnKGTglSaqBs2olSaqBgVOSpBoYOCVJqoGBU+KfdudI+Xq6N1ex5Fo1fZ+R93lsfj47Iv7RynP+I18j9b0b8Xoj89c7fz2PDc0fu7KKflrdYaQtnyfVCz/HKa3rMeBiYCTwSeA1sg/DryMiuqaUVje/XqXTge6ttPkPsqULZwOPb+TrSGoHZpzSup5PKd1Etg4vwJ6wzu4uV0fEEmDXiPhQRPwhIv4REX+JiKPXdJJnmS9GxFP883qq3ydbrJyI6B4R34yIv+c7bNyf7wRzcd72x3kGNzAi3hMRd0fEq3n7L1W83qci4oWI+Dtvrvfbqoj4XmR7qr4eEc9GxMnNmmwSETfk731qRGyVP2+DY5HKzsAprWuTiHgH8LH8fE7FY5sC25Dt87gQmEy2bulFZJnhxIh4b0T8O1ngm0+2ktIBLbzeWfkxg2zFpT+SrVpzY/74tWR7Ri4GfgUMBr5Dtsj+ZRExPCL6AhPIFtG/iGyx/Wo9TbYG7BnAAuCqfIHzNd4NzM3HczDwtYjotqGx1PC6Ut2yVCuta812aZCtt3t2s8c/l1JaEhGHkq1h+nayrdXW2J83FwX/bkppQkQM4M0MtrnhZAuKfzKltHTNxYh4nGyj8IdTSjdHxM7AoPzhyoXzDyT7A7gHcF1KaXxErAZ+VOX7fRdZwK7c03Qwb/7BMC+ldE5EdCcrWQ8FdmphLHdU+bpS3TJwSut6mCwDexl4qtkyf6+llJqvj3oD8NOK89nAofn30exrLTa0Msld5Bs05+aTb8Rc6+tFxL+Rrc36ONl7PpJsjeBqdxxZ31ik0jNwSut6MaV0bxXt/kAWXIeR7f7SDTiMLAO7L2/zxYjoQrYI+IbcQbap9y0RcRuwW0rpi2SlWYCDI2IZ8D9kez8OIduQeRlZCfh2sgC2AjguIuaQbfRci57AANZfUu4fEd8g2wGkS/7eZrYwFmfTqvS8xylthJTSy2SBchbwLbKMbRkwO6X0J7LdYvqRzaD9XQtdfSs/dgGuBt6fX58E/D+yLPBnKaVVZFusPUhW9r0Q2ByYnlJaQFZG7QJ8lWwrrGrew5+B75Lt7DEa+N/1NPtL/vgngDuBC1saSzWvK9U716qVJKkGZpySJNXAwClJUg0MnJIk1cDAKUlSDQyckiTVwMApSVINDJySJNXg/wNPrexBaE2cpAAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 504x504 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "Fold  2\n",
            "                   \n",
            "Epoch 1/50\n",
            "10/10 [==============================] - 20s 2s/step - loss: 0.6387 - accuracy: 0.6967 - val_loss: 0.5032 - val_accuracy: 0.8032\n",
            "Epoch 2/50\n",
            "10/10 [==============================] - 17s 2s/step - loss: 0.4990 - accuracy: 0.8056 - val_loss: 0.4043 - val_accuracy: 0.8337\n",
            "Epoch 3/50\n",
            "10/10 [==============================] - 17s 2s/step - loss: 0.4144 - accuracy: 0.8273 - val_loss: 0.3229 - val_accuracy: 0.8743\n",
            "Epoch 4/50\n",
            "10/10 [==============================] - 17s 2s/step - loss: 0.3364 - accuracy: 0.8610 - val_loss: 0.2460 - val_accuracy: 0.8919\n",
            "Epoch 5/50\n",
            "10/10 [==============================] - 17s 2s/step - loss: 0.2501 - accuracy: 0.8852 - val_loss: 0.1447 - val_accuracy: 0.9027\n",
            "Epoch 6/50\n",
            "10/10 [==============================] - 17s 2s/step - loss: 0.1534 - accuracy: 0.9441 - val_loss: 0.0684 - val_accuracy: 0.9948\n",
            "Epoch 7/50\n",
            "10/10 [==============================] - 17s 2s/step - loss: 0.0851 - accuracy: 0.9900 - val_loss: 0.0357 - val_accuracy: 0.9970\n",
            "Epoch 8/50\n",
            "10/10 [==============================] - 17s 2s/step - loss: 0.0482 - accuracy: 0.9959 - val_loss: 0.0197 - val_accuracy: 0.9988\n",
            "Epoch 9/50\n",
            "10/10 [==============================] - 17s 2s/step - loss: 0.0293 - accuracy: 0.9974 - val_loss: 0.0125 - val_accuracy: 0.9990\n",
            "Epoch 10/50\n",
            "10/10 [==============================] - 17s 2s/step - loss: 0.0197 - accuracy: 0.9981 - val_loss: 0.0085 - val_accuracy: 0.9991\n",
            "Epoch 11/50\n",
            "10/10 [==============================] - 17s 2s/step - loss: 0.0145 - accuracy: 0.9985 - val_loss: 0.0062 - val_accuracy: 0.9992\n",
            "Epoch 12/50\n",
            "10/10 [==============================] - 17s 2s/step - loss: 0.0114 - accuracy: 0.9987 - val_loss: 0.0049 - val_accuracy: 0.9994\n",
            "Epoch 13/50\n",
            "10/10 [==============================] - 17s 2s/step - loss: 0.0093 - accuracy: 0.9989 - val_loss: 0.0040 - val_accuracy: 0.9995\n",
            "Epoch 14/50\n",
            "10/10 [==============================] - 17s 2s/step - loss: 0.0079 - accuracy: 0.9991 - val_loss: 0.0034 - val_accuracy: 0.9995\n",
            "Epoch 15/50\n",
            "10/10 [==============================] - 17s 2s/step - loss: 0.0069 - accuracy: 0.9992 - val_loss: 0.0029 - val_accuracy: 0.9995\n",
            "Epoch 16/50\n",
            "10/10 [==============================] - 17s 2s/step - loss: 0.0062 - accuracy: 0.9992 - val_loss: 0.0026 - val_accuracy: 0.9995\n",
            "Epoch 17/50\n",
            "10/10 [==============================] - 17s 2s/step - loss: 0.0058 - accuracy: 0.9992 - val_loss: 0.0024 - val_accuracy: 0.9995\n",
            "Epoch 18/50\n",
            "10/10 [==============================] - 17s 2s/step - loss: 0.0053 - accuracy: 0.9993 - val_loss: 0.0022 - val_accuracy: 0.9995\n",
            "Epoch 19/50\n",
            "10/10 [==============================] - 17s 2s/step - loss: 0.0049 - accuracy: 0.9994 - val_loss: 0.0022 - val_accuracy: 0.9996\n",
            "Epoch 20/50\n",
            "10/10 [==============================] - 17s 2s/step - loss: 0.0046 - accuracy: 0.9994 - val_loss: 0.0021 - val_accuracy: 0.9996\n",
            "Epoch 21/50\n",
            "10/10 [==============================] - 17s 2s/step - loss: 0.0044 - accuracy: 0.9994 - val_loss: 0.0020 - val_accuracy: 0.9996\n",
            "Epoch 22/50\n",
            "10/10 [==============================] - 17s 2s/step - loss: 0.0042 - accuracy: 0.9995 - val_loss: 0.0019 - val_accuracy: 0.9996\n",
            "Epoch 23/50\n",
            "10/10 [==============================] - 17s 2s/step - loss: 0.0041 - accuracy: 0.9995 - val_loss: 0.0019 - val_accuracy: 0.9996\n",
            "Epoch 24/50\n",
            "10/10 [==============================] - 17s 2s/step - loss: 0.0039 - accuracy: 0.9995 - val_loss: 0.0018 - val_accuracy: 0.9996\n",
            "Epoch 25/50\n",
            "10/10 [==============================] - 17s 2s/step - loss: 0.0038 - accuracy: 0.9995 - val_loss: 0.0018 - val_accuracy: 0.9996\n",
            "Epoch 26/50\n",
            "10/10 [==============================] - 18s 2s/step - loss: 0.0037 - accuracy: 0.9995 - val_loss: 0.0017 - val_accuracy: 0.9996\n",
            "Epoch 27/50\n",
            "10/10 [==============================] - 17s 2s/step - loss: 0.0037 - accuracy: 0.9995 - val_loss: 0.0017 - val_accuracy: 0.9996\n",
            "Epoch 28/50\n",
            "10/10 [==============================] - 17s 2s/step - loss: 0.0036 - accuracy: 0.9995 - val_loss: 0.0017 - val_accuracy: 0.9996\n",
            "Epoch 29/50\n",
            "10/10 [==============================] - 17s 2s/step - loss: 0.0035 - accuracy: 0.9995 - val_loss: 0.0017 - val_accuracy: 0.9996\n",
            "Epoch 30/50\n",
            "10/10 [==============================] - 17s 2s/step - loss: 0.0034 - accuracy: 0.9996 - val_loss: 0.0016 - val_accuracy: 0.9996\n",
            "Epoch 31/50\n",
            "10/10 [==============================] - 17s 2s/step - loss: 0.0034 - accuracy: 0.9995 - val_loss: 0.0016 - val_accuracy: 0.9996\n",
            "Epoch 32/50\n",
            "10/10 [==============================] - 17s 2s/step - loss: 0.0034 - accuracy: 0.9995 - val_loss: 0.0016 - val_accuracy: 0.9996\n",
            "Epoch 33/50\n",
            "10/10 [==============================] - 17s 2s/step - loss: 0.0033 - accuracy: 0.9995 - val_loss: 0.0016 - val_accuracy: 0.9996\n",
            "Epoch 34/50\n",
            "10/10 [==============================] - 17s 2s/step - loss: 0.0033 - accuracy: 0.9995 - val_loss: 0.0016 - val_accuracy: 0.9996\n",
            "Epoch 35/50\n",
            "10/10 [==============================] - 17s 2s/step - loss: 0.0033 - accuracy: 0.9995 - val_loss: 0.0016 - val_accuracy: 0.9996\n",
            "Epoch 36/50\n",
            "10/10 [==============================] - 17s 2s/step - loss: 0.0033 - accuracy: 0.9995 - val_loss: 0.0016 - val_accuracy: 0.9996\n",
            "Epoch 37/50\n",
            "10/10 [==============================] - 17s 2s/step - loss: 0.0032 - accuracy: 0.9996 - val_loss: 0.0016 - val_accuracy: 0.9996\n",
            "Epoch 38/50\n",
            "10/10 [==============================] - 17s 2s/step - loss: 0.0032 - accuracy: 0.9996 - val_loss: 0.0016 - val_accuracy: 0.9996\n",
            "Epoch 39/50\n",
            "10/10 [==============================] - 17s 2s/step - loss: 0.0032 - accuracy: 0.9995 - val_loss: 0.0016 - val_accuracy: 0.9996\n",
            "Epoch 40/50\n",
            "10/10 [==============================] - 17s 2s/step - loss: 0.0032 - accuracy: 0.9995 - val_loss: 0.0016 - val_accuracy: 0.9996\n",
            "Epoch 41/50\n",
            "10/10 [==============================] - 17s 2s/step - loss: 0.0031 - accuracy: 0.9996 - val_loss: 0.0016 - val_accuracy: 0.9996\n",
            "Epoch 42/50\n",
            "10/10 [==============================] - 17s 2s/step - loss: 0.0031 - accuracy: 0.9996 - val_loss: 0.0016 - val_accuracy: 0.9996\n",
            "Epoch 43/50\n",
            "10/10 [==============================] - 17s 2s/step - loss: 0.0030 - accuracy: 0.9996 - val_loss: 0.0016 - val_accuracy: 0.9996\n",
            "Epoch 44/50\n",
            "10/10 [==============================] - 18s 2s/step - loss: 0.0031 - accuracy: 0.9996 - val_loss: 0.0016 - val_accuracy: 0.9996\n",
            "Epoch 45/50\n",
            "10/10 [==============================] - 17s 2s/step - loss: 0.0031 - accuracy: 0.9996 - val_loss: 0.0015 - val_accuracy: 0.9996\n",
            "Epoch 46/50\n",
            "10/10 [==============================] - 17s 2s/step - loss: 0.0031 - accuracy: 0.9996 - val_loss: 0.0015 - val_accuracy: 0.9996\n",
            "Epoch 47/50\n",
            "10/10 [==============================] - 17s 2s/step - loss: 0.0031 - accuracy: 0.9996 - val_loss: 0.0015 - val_accuracy: 0.9996\n",
            "Epoch 48/50\n",
            "10/10 [==============================] - 17s 2s/step - loss: 0.0030 - accuracy: 0.9996 - val_loss: 0.0015 - val_accuracy: 0.9996\n",
            "Epoch 49/50\n",
            "10/10 [==============================] - 17s 2s/step - loss: 0.0031 - accuracy: 0.9996 - val_loss: 0.0015 - val_accuracy: 0.9996\n",
            "Epoch 50/50\n",
            "10/10 [==============================] - 17s 2s/step - loss: 0.0030 - accuracy: 0.9996 - val_loss: 0.0015 - val_accuracy: 0.9996\n",
            "17675/17675 [==============================] - 27s 2ms/step - loss: 0.0015 - accuracy: 0.9996\n",
            "CV Evaluate: [0.001536453957669437, 0.9995579719543457]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 864x432 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 504x504 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 504x504 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "Fold  3\n",
            "                   \n",
            "Epoch 1/50\n",
            "10/10 [==============================] - 20s 2s/step - loss: 0.6375 - accuracy: 0.7171 - val_loss: 0.5188 - val_accuracy: 0.8032\n",
            "Epoch 2/50\n",
            "10/10 [==============================] - 17s 2s/step - loss: 0.4882 - accuracy: 0.8032 - val_loss: 0.4055 - val_accuracy: 0.8032\n",
            "Epoch 3/50\n",
            "10/10 [==============================] - 17s 2s/step - loss: 0.4059 - accuracy: 0.8030 - val_loss: 0.3338 - val_accuracy: 0.8033\n",
            "Epoch 4/50\n",
            "10/10 [==============================] - 17s 2s/step - loss: 0.3302 - accuracy: 0.8085 - val_loss: 0.2565 - val_accuracy: 0.8686\n",
            "Epoch 5/50\n",
            "10/10 [==============================] - 17s 2s/step - loss: 0.2440 - accuracy: 0.8343 - val_loss: 0.1741 - val_accuracy: 0.8935\n",
            "Epoch 6/50\n",
            "10/10 [==============================] - 18s 2s/step - loss: 0.1692 - accuracy: 0.9000 - val_loss: 0.1228 - val_accuracy: 0.9943\n",
            "Epoch 7/50\n",
            "10/10 [==============================] - 17s 2s/step - loss: 0.1222 - accuracy: 0.9739 - val_loss: 0.0900 - val_accuracy: 0.9985\n",
            "Epoch 8/50\n",
            "10/10 [==============================] - 17s 2s/step - loss: 0.0898 - accuracy: 0.9940 - val_loss: 0.0687 - val_accuracy: 0.9963\n",
            "Epoch 9/50\n",
            "10/10 [==============================] - 17s 2s/step - loss: 0.0674 - accuracy: 0.9972 - val_loss: 0.0524 - val_accuracy: 0.9952\n",
            "Epoch 10/50\n",
            "10/10 [==============================] - 17s 2s/step - loss: 0.0510 - accuracy: 0.9981 - val_loss: 0.0397 - val_accuracy: 0.9947\n",
            "Epoch 11/50\n",
            "10/10 [==============================] - 17s 2s/step - loss: 0.0390 - accuracy: 0.9985 - val_loss: 0.0308 - val_accuracy: 0.9942\n",
            "Epoch 12/50\n",
            "10/10 [==============================] - 17s 2s/step - loss: 0.0300 - accuracy: 0.9988 - val_loss: 0.0251 - val_accuracy: 0.9939\n",
            "Epoch 13/50\n",
            "10/10 [==============================] - 16s 2s/step - loss: 0.0235 - accuracy: 0.9990 - val_loss: 0.0214 - val_accuracy: 0.9936\n",
            "Epoch 14/50\n",
            "10/10 [==============================] - 17s 2s/step - loss: 0.0187 - accuracy: 0.9991 - val_loss: 0.0194 - val_accuracy: 0.9927\n",
            "Epoch 15/50\n",
            "10/10 [==============================] - 17s 2s/step - loss: 0.0151 - accuracy: 0.9992 - val_loss: 0.0186 - val_accuracy: 0.9917\n",
            "Epoch 16/50\n",
            "10/10 [==============================] - 17s 2s/step - loss: 0.0127 - accuracy: 0.9992 - val_loss: 0.0175 - val_accuracy: 0.9919\n",
            "Epoch 17/50\n",
            "10/10 [==============================] - 17s 2s/step - loss: 0.0110 - accuracy: 0.9994 - val_loss: 0.0163 - val_accuracy: 0.9926\n",
            "Epoch 18/50\n",
            "10/10 [==============================] - 17s 2s/step - loss: 0.0099 - accuracy: 0.9994 - val_loss: 0.0156 - val_accuracy: 0.9931\n",
            "Epoch 19/50\n",
            "10/10 [==============================] - 17s 2s/step - loss: 0.0090 - accuracy: 0.9995 - val_loss: 0.0148 - val_accuracy: 0.9938\n",
            "Epoch 20/50\n",
            "10/10 [==============================] - 17s 2s/step - loss: 0.0083 - accuracy: 0.9995 - val_loss: 0.0132 - val_accuracy: 0.9954\n",
            "Epoch 21/50\n",
            "10/10 [==============================] - 17s 2s/step - loss: 0.0078 - accuracy: 0.9996 - val_loss: 0.0130 - val_accuracy: 0.9955\n",
            "Epoch 22/50\n",
            "10/10 [==============================] - 17s 2s/step - loss: 0.0074 - accuracy: 0.9996 - val_loss: 0.0130 - val_accuracy: 0.9954\n",
            "Epoch 23/50\n",
            "10/10 [==============================] - 17s 2s/step - loss: 0.0071 - accuracy: 0.9996 - val_loss: 0.0132 - val_accuracy: 0.9953\n",
            "Epoch 24/50\n",
            "10/10 [==============================] - 17s 2s/step - loss: 0.0068 - accuracy: 0.9996 - val_loss: 0.0134 - val_accuracy: 0.9951\n",
            "Epoch 25/50\n",
            "10/10 [==============================] - 17s 2s/step - loss: 0.0066 - accuracy: 0.9996 - val_loss: 0.0135 - val_accuracy: 0.9950\n",
            "Epoch 26/50\n",
            "10/10 [==============================] - 17s 2s/step - loss: 0.0062 - accuracy: 0.9997 - val_loss: 0.0136 - val_accuracy: 0.9949\n",
            "Epoch 27/50\n",
            "10/10 [==============================] - 17s 2s/step - loss: 0.0060 - accuracy: 0.9996 - val_loss: 0.0136 - val_accuracy: 0.9948\n",
            "Epoch 28/50\n",
            "10/10 [==============================] - 17s 2s/step - loss: 0.0059 - accuracy: 0.9997 - val_loss: 0.0137 - val_accuracy: 0.9948\n",
            "Epoch 29/50\n",
            "10/10 [==============================] - 17s 2s/step - loss: 0.0057 - accuracy: 0.9997 - val_loss: 0.0137 - val_accuracy: 0.9947\n",
            "Epoch 30/50\n",
            "10/10 [==============================] - 17s 2s/step - loss: 0.0057 - accuracy: 0.9997 - val_loss: 0.0138 - val_accuracy: 0.9947\n",
            "Epoch 31/50\n",
            "10/10 [==============================] - 17s 2s/step - loss: 0.0055 - accuracy: 0.9997 - val_loss: 0.0138 - val_accuracy: 0.9947\n",
            "Epoch 32/50\n",
            "10/10 [==============================] - 17s 2s/step - loss: 0.0054 - accuracy: 0.9997 - val_loss: 0.0138 - val_accuracy: 0.9947\n",
            "Epoch 33/50\n",
            "10/10 [==============================] - 17s 2s/step - loss: 0.0054 - accuracy: 0.9997 - val_loss: 0.0138 - val_accuracy: 0.9947\n",
            "Epoch 34/50\n",
            "10/10 [==============================] - 17s 2s/step - loss: 0.0053 - accuracy: 0.9997 - val_loss: 0.0138 - val_accuracy: 0.9947\n",
            "Epoch 35/50\n",
            "10/10 [==============================] - 17s 2s/step - loss: 0.0052 - accuracy: 0.9997 - val_loss: 0.0138 - val_accuracy: 0.9947\n",
            "Epoch 36/50\n",
            "10/10 [==============================] - 17s 2s/step - loss: 0.0051 - accuracy: 0.9997 - val_loss: 0.0138 - val_accuracy: 0.9947\n",
            "Epoch 37/50\n",
            "10/10 [==============================] - 17s 2s/step - loss: 0.0051 - accuracy: 0.9997 - val_loss: 0.0138 - val_accuracy: 0.9947\n",
            "Epoch 38/50\n",
            "10/10 [==============================] - 17s 2s/step - loss: 0.0050 - accuracy: 0.9997 - val_loss: 0.0138 - val_accuracy: 0.9947\n",
            "Epoch 39/50\n",
            "10/10 [==============================] - 17s 2s/step - loss: 0.0050 - accuracy: 0.9997 - val_loss: 0.0138 - val_accuracy: 0.9947\n",
            "Epoch 40/50\n",
            "10/10 [==============================] - 17s 2s/step - loss: 0.0049 - accuracy: 0.9997 - val_loss: 0.0138 - val_accuracy: 0.9947\n",
            "Epoch 41/50\n",
            "10/10 [==============================] - 17s 2s/step - loss: 0.0049 - accuracy: 0.9997 - val_loss: 0.0138 - val_accuracy: 0.9947\n",
            "Epoch 42/50\n",
            "10/10 [==============================] - 17s 2s/step - loss: 0.0048 - accuracy: 0.9997 - val_loss: 0.0138 - val_accuracy: 0.9947\n",
            "Epoch 43/50\n",
            "10/10 [==============================] - 17s 2s/step - loss: 0.0049 - accuracy: 0.9997 - val_loss: 0.0138 - val_accuracy: 0.9947\n",
            "Epoch 44/50\n",
            "10/10 [==============================] - 17s 2s/step - loss: 0.0048 - accuracy: 0.9997 - val_loss: 0.0138 - val_accuracy: 0.9947\n",
            "Epoch 45/50\n",
            "10/10 [==============================] - 17s 2s/step - loss: 0.0048 - accuracy: 0.9997 - val_loss: 0.0138 - val_accuracy: 0.9947\n",
            "Epoch 46/50\n",
            "10/10 [==============================] - 17s 2s/step - loss: 0.0047 - accuracy: 0.9997 - val_loss: 0.0138 - val_accuracy: 0.9947\n",
            "Epoch 47/50\n",
            "10/10 [==============================] - 17s 2s/step - loss: 0.0047 - accuracy: 0.9997 - val_loss: 0.0138 - val_accuracy: 0.9947\n",
            "Epoch 48/50\n",
            "10/10 [==============================] - 20s 2s/step - loss: 0.0048 - accuracy: 0.9997 - val_loss: 0.0138 - val_accuracy: 0.9947\n",
            "Epoch 49/50\n",
            "10/10 [==============================] - 17s 2s/step - loss: 0.0047 - accuracy: 0.9997 - val_loss: 0.0138 - val_accuracy: 0.9947\n",
            "Epoch 50/50\n",
            "10/10 [==============================] - 17s 2s/step - loss: 0.0047 - accuracy: 0.9997 - val_loss: 0.0138 - val_accuracy: 0.9947\n",
            "17675/17675 [==============================] - 28s 2ms/step - loss: 0.0138 - accuracy: 0.9947\n",
            "CV Evaluate: [0.013824635185301304, 0.9947009682655334]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 864x432 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 504x504 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAc4AAAGtCAYAAACfhpREAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+WH4yJAAAgAElEQVR4nO3debxVVdnA8d8DQkCKmii+CoYD2YtDWQ71OuGUaCKaVGKWOKEplpUaZjm+ZTmUs0ZpjjlkWaiUqWmpbyCU5oBhaIg44QwJKHDX+8fe4OEK956D9+57zz6/b5/9uXfvs8466+DtPvd59jprRUoJSZJUnS4dPQBJkuqJgVOSpBoYOCVJqoGBU5KkGhg4JUmqgYFTkqQaGDglSaUUEVdExKyIeGw5j0dEXBAR0yLikYj4RDX9GjglSWV1JTCkhcf3AAbmxyjg0mo6NXBKkkoppfQX4LUWmgwDrk6ZCcBqEfFfrfW7UlsNsJH1/OTXXX5JhXl1wnkdPQQ1kF7dItqyv55bjG6z35fzH774CLJMcbGxKaWxNXSxLvBsxfnM/NoLLT3JwClJqkt5kKwlULYJA6ckqTjRqe4QPgf0rzjvl19rUad6B5IkFWgc8JV8du2ngDdTSi2WacGMU5JUpLa9ZdrKS8X1wGCgT0TMBE4BugGklC4DxgN7AtOAucDB1fRr4JQkFafAUm1KaUQrjyfg6Fr7tVQrSVINzDglScUpsFTbXgyckqTidK5ZtSuk/t+BJEkFMuOUJBXHUq0kSTWwVCtJUmMx45QkFcdSrSRJNbBUK0lSYzHjlCQVx1KtJEk1sFQrSVJjMeOUJBXHUq0kSTWwVCtJUmMx45QkFacEGaeBU5JUnC71f4+z/kO/JEkFMuOUJBXHUq0kSTUowcdR6j/0S5JUIDNOSVJxLNVKklQDS7WSJDUWM05JUnEs1UqSVIMSlGoNnJKk4pQg46z/dyBJUoHMOCVJxbFUK0lSDSzVSpLUWMw4JUnFsVQrSVINLNVKktRYzDglScUpQcZp4JQkFacE9zjrP/RLklQgM05JUnEs1UqSVANLtZIkNRYzTklScSzVSpJUA0u1kiQ1FjNOSVJhogQZp4FTklSYMgROS7WSJNXAjFOSVJz6TzgNnJKk4liqlSSpwZhxSpIKU4aM08ApSSpMGQKnpVpJkmpgxilJKkwZMk4DpySpOPUfNy3VSpJUCzNOSVJhLNVKklSDMgROS7WSJNXAjFOSVJgyZJwGTklSYcoQOC3VSpJUAzNOSVJx6j/hNHBKkopjqVaSpAZjxilJKkwZMk4DpySpMGUInJZqJUmlFRFDImJqREyLiDHLeHy9iLgnIh6KiEciYs/W+jRwSpKKE214tPZSEV2Bi4E9gEHAiIgY1KzZd4GbUkpbAPsDl7TWr6VaSVJhCi7Vbg1MSyk9nb/2DcAwYEpFmwT0zr9fFXi+tU7NOCVJdSkiRkXE5IpjVLMm6wLPVpzPzK9VOhU4MCJmAuOBY1p7XTNOSVJh2jLjTCmNBca+z25GAFemlM6NiE8D10TEpimlpuU9wcApSSpMwaXa54D+Fef98muVDgWGAKSU/hoRPYA+wKzldWqpVpJUVpOAgRGxfkR0J5v8M65ZmxnALgAR8d9AD+Dlljo145QkFabIjDOltDAiRgN3AF2BK1JKj0fE6cDklNI44FvAzyLiG2QThUamlFJL/Ro4JUnFKXj9g5TSeLJJP5XXTq74fgqwbS19WqqVJKkGZpySpMKUYck9A6ckqTBlCJyWaiVJqoEZpySpMGXIOA2ckqTi1H/cNHBKkopThozTe5ySJNXAjFOSVBgzTmk5jh6xI5NvHMPfbhrD6BE7ArDZwHW49xfHMunGb3PzTw5nlQ9+AIBPf2x9Hrzh29x/zbfYsP+aAKy6ck9uvfirpfg/mYp16ne/w847/A/D9xm65Nr5Pz6HL+y7N9898dtLrt1+6ziuu+aqjhhiQ4uINjs6SqcLnBExICJSfjRFxMsR8cuIWPl99Hlv3l+fthyrlm3Qhv/Fwft8mu0POpetR5zFHttvwgb9+nDp90bw3QtvZasv/ohx9zzCN76yCwBfP3An9v3aZZxw7i0cPjxb+WrMYZ/hrCvupJUlI6X3GLrPvlx82c+WnM+ZM4cnpkzhplvG0a1bN/715FTmz5/PuN/+hi/sf0AHjlT1qtMFzgoPAV8C7iPbL+2r76Ov0/M+ZrfBuNSKj67fl0mPPcO8+QtYtKiJ+/4+jX123pyNPrwm9//9KQD+NHEq++z8MQAWLFxEzx7d6dmjGwsWLmL9fmvQr+9q3Pe3aR35NlSnPrnlVqy66qpLzrt0CRYuXEBKifnz57HSSt24+sor2P+AA+nWrVsHjrQxmXG2r5eBu8i2hQHoHRHrRsSvI+L1iHg+In4YEV0BImJ6RLwVEedHxGsRMSki1s6fezJwfd5HRMS5EfFGREyIiFvybHRw3k+KiH9FxHUR8WZE/DEiehX83uva49NeYNstNuBDq/aiZ49uDNl2EP36rs4TT73I0MGbAfC5XT9Ov76rAXD2L+7i8tMP5PiDd+OyG//CaUftxamXjG/pJaSqffCDK7PdDjuy//B96bPmmqy8yso89sg/2GmXXTt6aI0p2vDoIJ15ctBneHcj0eeBi4AbgE8CPwbWBr5NtinphXm7XkBv4HbgQOBw4Ixm/Q4FvgncD9wEnLmM194IuAX4K7A7sB9wTWWDiBgFjAJYab2dWanPpiv2Lkto6vSXOPequ7n14qOYO+9t/vHkcyxqauKI03/Jucfvx5jDduf2Pz/GOwsWAfDIk8+x48ifALDtFhvy4iuziYBrzjyIBQubGPOT3zLrtTkd+ZZU50YechgjDzkMgNNO/i5fHf01fnPzr5jw1wcY+JGNOfyI91PQUqPpzBnnRGA34PvAOsDxwI7AKsApwBF5u90qntNEVtK9OD8fsIx+d8q/npZSuhD43TLavJBSOgG4cnn9pJTGppS2TCltadB8r6t+N4FtDzyH3Q6/kDdmz+NfM17myemzGHr0pWx74DncdMff+PfMV97zvDGHfYYzf34HJ40awknnj+OKW/6Po/bfoQPegcron09MIaXEgAHrc9cf/8BZ557HzGdn8Mwz0zt6aA3DUm37eiWldBdZ4ATYmSw5/wdZsFx8VGaU81JK84GF+XnXFvpvadbJa/nXavrRMqy5ejaXq//aqzNs58258fd/W3ItIhhz6Gf42a8fWOo5X9prK+64fwqvz55Lrx7daUqJ1JTo1aN74eNXOV1y4fkcdczXWLhwIYuamgCI6ML8efM7eGSNowyBszOXateJiP2B7fPzacCbwA75teeA7YB/8u590GrcAxwLnBwRg4BhbTZiLXH92YfwoVU/yIKFizj2hzfz5n/mcfSIHTni89sB8Lt7HuHqcROXtO/ZoxtfHroNex19CQAXXHsPt1xwBO8sWMTIk67ukPeg+jTm+G/yt0mTeOON19l9lx058qhj2He/4dxz910M2mRT1lqrLwAbb/xRPr/vUAZ+ZGM2/uhHO3jUqifR2ab7R8QA4N8Vl2aT3Ws8giwDPI8s++wGPAqcklK6KyKmA31SSitHxJZkwfSqlNLIiLiXrMy7JvAqcA5wCPA48B+y+5hbpJQejogEPJ5S2jQihgO/Iivrnrq8Mff85Nc71z+iSu3VCed19BDUQHp1a9vUbqPjft9mvy+nnbNHh6SdnS7jTClNp+X5Up9fzvMGVHw/ubKPlNLgyrYR8Vzez9rA+WTZ6xN528rn3dzKWCRJNSjDoiadLnAWZG/gf4F3gMnA8Smltzt2SJKketCQgbN5BipJKkYJEs7GDJySpI5RhlJtZ/44iiRJnY4ZpySpMCVIOA2ckqTidOlS/5HTUq0kSTUw45QkFcZSrSRJNXBWrSRJDcaMU5JUmBIknAZOSVJxLNVKktRgzDglSYUpQ8Zp4JQkFaYEcdNSrSRJtTDjlCQVxlKtJEk1KEHctFQrSVItzDglSYWxVCtJUg1KEDct1UqSVAszTklSYSzVSpJUgxLETUu1kiTVwoxTklQYS7WSJNWgBHHTUq0kSbUw45QkFcZSrSRJNShB3LRUK0lSLcw4JUmFsVQrSVINShA3LdVKklQLM05JUmEs1UqSVIMyBE5LtZIk1cCMU5JUmBIknAZOSVJxLNVKktRgzDglSYUpQcJp4JQkFacMpVoDpySpMCWIm97jlCSpFmackqTCdClBymnglCQVpgRx01KtJEm1MOOUJBWmDLNqzTglSYXpEm13VCMihkTE1IiYFhFjltPmCxExJSIej4hfttanGackqZQioitwMbAbMBOYFBHjUkpTKtoMBE4Etk0pvR4Ra7XWr4FTklSYgku1WwPTUkpP5699AzAMmFLR5nDg4pTS6wAppVmtdWqpVpJUmIi2PGJUREyuOEY1e7l1gWcrzmfm1yp9BPhIRDwQERMiYkhr78GMU5JUl1JKY4Gx77OblYCBwGCgH/CXiNgspfRGS0+QJKkQQaGl2ueA/hXn/fJrlWYCE1NKC4B/R8STZIF00vI6tVQrSSpMwbNqJwEDI2L9iOgO7A+Ma9bmt2TZJhHRh6x0+3SL76HG9yxJUl1IKS0ERgN3AE8AN6WUHo+I0yNi77zZHcCrETEFuAc4PqX0akv9WqqVJBWm6AUQUkrjgfHNrp1c8X0CvpkfVTFwSpIKU4KFgyzVSpJUCzNOSVJh3FZMkqQalCBuWqqVJKkWZpySpMKUYVux5QbOiPhES09MKf297YcjSSqzEsTNFjPOyUBq4fGubTwWSZI6vZYC59W0HDglSapJqWfVppRGFjgOSVIDqP+wWcWs2ohYIyJ+FRGvR8Su+fdHFzE4SZI6m2o+jnIpMAToDTQB04Ej23FMkqSSiog2OzpKNYFzN+CcivMpwPrtMxxJUpkVvK1Y+7yHKtq8BfTNv+8K7Aq0uOWKJEllVc0CCDeQbbeSgNvy55zdnoOSJJVTqRdAqHAiMAf4bH5+G3Bmu41IklRaJYibrQfOlNKCiPgB8Ov80tSU0oL2HZYkSZ1TNR9H2ZlsJu0/8uPfETG4fYclSSqjMsyqraZU+3NgZeA6skA7FLgc2LAdxyVJKqGOnA3bVqqZVdsLGJNS+kpK6UCye57uqiJJakjV7I5yDfDFiHiCLNB+AbipgLFJkkqm7LNqK3dHCeDuiu+3B45vx3FJkkqo/sOmu6NIklQTd0eRJBWm1NuKLRYRHwS+BmwG9Mgvp5TSfu05MElS+ZQgblb9cZQvkpVtF79lS7iSpIZUTeDcFbgIOJosgH4eeKg9ByVJKqcyzKqt5nOcKwOPkGWbvYBJwHHtOShJUjlFtN3RUarJOGeSBc+nyFYMCuDZ9hyUJEmdVTWB80jgP8DDwA/J7m9+pz0HJUkqp4aYVZtSurvi9FPtOBZJUsmVIG62uOTe7Bael1JKq7bDeCRJ6tRayjhfw4+dVOX1ied39BDUQFbfanRHD0ENZN5DF7Vpf2WYVdvSykEDChyHJKkBVPNRjs6uDO9BkqTCuK+mJKkwpS7VSpLU1rrUf9ysLnBGRHdgEPDvlNKb7TskSVJZlSFwtnqPMyK2AJ4m29h6q4h4IiJ+1u4jkySpE6om47yIbOWgAJqAa4HD2nNQkqRyKsM9zmpm1X4MuLLi/HlgrXYZjSSp1LpE2x0dpdpF3nfMv98cGAFMb68BSZLUmVUTOM8i28wa4Fyyku3I9hqQJKm8SlCprWqR9ysi4mlgz/zS7SmlP7fvsCRJZdQQu6NExHpks2ovqryWUprRngOTJKkzqqZUO533LvaeqnyuJElLlGGd12qC33jeDZyrA9sAk9ptRJKk0ipBpbaqe5x7VZ5HxGHAvu02IkmSOrFq7nF+s1n7zwIfb7cRSZJKqyEmBwHnLOPa9W09EElS+ZUgblYVOA+u+H4RMD2ldH87jUeSpE6txcAZEV2Bo4GfpJTMMiVJ70sZdkdpMXCmlBZFRALWK2g8kqQSa5R7nK8Ap0XEVmQLvAOklNLX229YkiR1TtUEzj3yr5+ruJYAA6ckqSYlSDiXHzjz9WmPAXYqbjiSpDIr+z3OAcAHU0q3FzQWSZI6vdZKtTtGRI9lPZBSurodxiNJKrGg/lPO1gLnkflRKcjucRo4JUk1KXupFuCXwMNFDESSpHrQWuC8NaV0UyEjkSSVXtkzzmeAt4oaiCSp/KIEn0dZbuBMKa1f5EAkSaoH1SyAIElSmyh7qVaSpDZVgkotXTp6AJIk1RMzTklSYRpldxRJktpEGe5xWqqVJJVWRAyJiKkRMS0ixrTQbr+ISBGxZWt9mnFKkgpTZKU2IroCFwO7ATOBSRExLqU0pVm7Vci2ypxYTb9mnJKkwnQh2uyowtbAtJTS0ymld4AbgGHLaHcG8CNgfnXvQZKkOhQRoyJicsUxqlmTdYFnK85n5tcq+/gE0L+WLTQt1UqSCtOWpdqU0lhg7IqPJboAPwZG1vI8A6ckqTAFz6p9Duhfcd4vv7bYKsCmwL35GrprA+MiYu+U0uTldWqpVpJUVpOAgRGxfkR0B/YHxi1+MKX0ZkqpT0ppQEppADABaDFoghmnJKlARS6AkFJaGBGjgTuArsAVKaXHI+J0YHJKaVzLPSybgVOSVJiiFw5KKY0Hxje7dvJy2g6upk9LtZIk1cCMU5JUGNeqlSSpBiWIm5ZqJUmqhRmnJKkwZcjWDJySpMJECWq1ZQj+kiQVxoxTklSY+s83DZySpAKV4eMolmolSaqBGackqTD1n28aOCVJBSpBpdZSrSRJtTDjlCQVpgyf4zRwSpIKU4Yyp4FTklSYMmScZQj+kiQVxoxTklSY+s83DZySpAJZqpUkqcGYcUqSClOGbM3AKUkqjKVaSZIajBmnJKkw9Z9vGjglSQUqQaXWUq0kSbUw45QkFaZLCYq1Bk5JUmEs1UqS1GDMOCVJhQlLtZIkVc9SrSRJDcaMU5JUGGfVSpJUA0u1kiQ1GDNOSVJhypBxGjglSYUpw8dRLNVKklQDM05JUmG61H/CaeCUJBXHUq0kSQ3GjFOSVBhn1UqSVANLtZIkNRgzTklSYZxVK0lSDSzVSpLUYAycancP3PcX9v7s7uw1ZDcu/9lYAE484VsM33coF5z34yXtxl52CX+6+66OGqbqyGWnfIln7j6Tyb/6zpJrq/fuxW2XjubR353MbZeOZrVVei557NwThvPY707hwRtP5OMf7QfAwA+vxQPXncCDN57INpuvD0DXrl24/bLR9OzRrdg31EAi2u7oKO0WOCNiQESk/PhexfXLF19v5fmn5u2G5+cpIh5r5TlX5u22bJt3ofdr0aJF/OD7p3PJZT/nlnG384fxt/Hk1H/ygR49uPmWW3n8sUeZM2cOL788i0cfeYSdd9m1o4esOnDNrRMYdvTFS1077uDduPfBqWw27HTufXAqxx38GQB2324QG663JpsOO43R/3s9F3xnfwAOG74dx599M/secynHfmUXAEZ9fnuuv30S8+YvKPYNNZBow6OjFJVxjozMysAXVrCPEcBxrbS5NG/31Aq+htrYY48+Qv/+H6Zf//50696dIXt+lvv+8mfenj+fpqYmFi5cSNcuXbjkwgs4avQxHT1c1YkH/v4Ur705d6lrew3enGtvnQjAtbdOZOhOm2fXd9ycX972IAAPPjqdVVfpydp9erNgwSJ69uhOzx7dWbBwEauu3JM9d9iU6/K20vIUETifBjYABpMFzW7AcwARsWlETImIuRHxRkSMj4h1l9PP9cA5+fN6R8RlEfF8/txr8zZfzdttmLc7PCL+FRFvRcSDEbFdfr15NvvY4gw4IjaOiIkRMS8iXo+I+9rh36RhzHrpJdb+r7WXnK/Vty8vvfQSq6/+IfYfvi87DN6JGTNm0JSa+O9Bm3TgSFXv1lpjFV58ZTYAL74ym7XWWAWAddZajZkvvr6k3XMvvcE6a63GT2/6Cyccujs/P+PLnHX5HZw4aghnXf5HUmqxGKb3qUtEmx0dpYhZtU8ALwOHAOsDvwU2BdYF3gGuAl4FBgAnAqcCh7fS53nAwcC1wL3Ah5s3iIidgbHAX8kC7mnAuIgY2ErfRwFbAV8HFgDbLatRRIwCRgFcdMlPOfTwUa10q0onnHjSku+POepIvnfqafzsp5fy5NR/8qlPb8t+n1/RwoSUaS3+Pfvi6+x++PkAbNC/D+uutRpT//0il5/xFbp368ppl9zOtBmzChhpY6n/ObXFfRzlCuAC4APAEODc/PoHgAOAzSvablZFf0OBWcBBKaWm5bTZM/96SkrpzohYD/gO8KlW+v4X2X/bPYFJwPnLapRSGksWmJm/EP9EXY61+vblxRdeXHI+66WX6Nu375Lze/50F4M22YS5c+fy7LMzOPvH53Pk4Yey515D6dmz57K6lJZp1qtzWLtPb158ZTZr9+nNy6/NAeD5WW/Qb+3Vl7Rbt+9qPD/rjaWee9rRQzn1kts4asRgfvHb/2PG869y2ui9Ofikqwp9D6oPRd3jvAFYBMwE7qy4fhJZ0BwDfIYsw+vRxq+dmn0lHwu8+4fDaksap3QRsCtZ0BwGTIiIjdt4TA1jk003Y8aM6cyc+SwL3nmHP4y/nR132hmABQsWcO3VVzHykMN4e/7bRF56aWpaxIIFTs5QbW7/86McOHQbAA4cug233fvIkusH7LU1AFtvNoDZ/5m3pKQLsN0nN+KFl9/kqRkv06tHN1JToqkp0atH9+LfRCMoweygQjLOlNLsiDgEmJNSaor31qbXAD5Hdv+zGreSlWqvioh7gQ+nlE5u1mY88C3gtIjYEDgUeB2YAHwobzM8IgaQlY0BiIgjgT7AtPzYHOgLTK1ybKqw0korceJJJ/PVUYfR1LSIffbdj402yqrlN15/HXsP25eePXvykY03Zv68+ey3z1C2234Hevfu3cEjV2d21Zkj2f6TA+mz2spM+8MZnHHZeM75xZ1c+6NDOGifTzPjhdc48IQrAPjD/Y+z+3ab8Pi4U5g7fwFHnHrtUn2NOWwIX/521vby3zzAL74/kpW6duHrP7ix8PfVCMqwAEK0143wPCD9G7g9pbRXs8ceAzYhC0o3Af3JSqJHA9NTSh+PiFOBU4DPp5RuzifvPJ5S2jQiegNnA3sDqwK3pJS+FBFXAgcBW6WUJkfE4cAJwDrAFOAbKaX7I6IX2b3WTwG3AdsAG6SUIiIOBr4L9APmkGXLX2uhJGypVoVafavRHT0ENZB5D13UppFu4lNvttnvy202XLVDonC7Bc5GYuBUkQycKlJbB84Hn267wLn1Bh0TOF2rVpJUmPov1LrkniRJNTHjlCQVpwQpp4FTklSYMsyqtVQrSVINzDglSYXpyO3A2oqBU5JUmBLETUu1kiTVwoxTklScEqScBk5JUmGcVStJUicWEUMiYmpETIuIMct4/JsRMSUiHomIuyPiPfs7N2fglCQVJqLtjtZfK7oCFwN7AIOAERExqFmzh4AtU0qbAzcDZ7XWr4FTklSYgrfj3BqYllJ6OqX0DtluV8MqG6SU7kkpzc1PJ5DtjNUiA6ckqThtGDkjYlRETK44RjV7tXWBZyvOZ1Kx//IyHAr8vrW34OQgSVJdSimNBca2RV8RcSCwJbBja20NnJKkwhQ8q/Y5oH/Feb/82lIiYlfgJGDHlNLbrXVq4JQkFabgJfcmAQMjYn2ygLk/cMDS44ktgJ8CQ1JKs6rp1HuckqRSSiktBEYDdwBPADellB6PiNMjYu+82dnAysCvIuLhiBjXWr9mnJKkwhS9/EFKaTwwvtm1kyu+37XWPg2ckqTi1P/CQZZqJUmqhRmnJKkwZVir1sApSSpMGTaytlQrSVINzDglSYUpQcJp4JQkFagEkdNSrSRJNTDjlCQVxlm1kiTVwFm1kiQ1GDNOSVJhSpBwGjglSQUqQeS0VCtJUg3MOCVJhXFWrSRJNXBWrSRJDcaMU5JUmBIknAZOSVKBShA5LdVKklQDM05JUmGcVStJUg2cVStJUoMx45QkFaYECaeBU5JUoBJETku1kiTVwIxTklQYZ9VKklQDZ9VKktRgzDglSYUpQcJp4JQkFcdSrSRJDcaMU5JUoPpPOQ2ckqTCWKqVJKnBmHFKkgpTgoTTwClJKo6lWkmSGowZpySpMK5VK0lSLeo/blqqlSSpFmackqTClCDhNHBKkorjrFpJkhqMGackqTDOqpUkqRb1Hzct1UqSVAszTklSYUqQcBo4JUnFKcOsWgOnJKkwZZgc5D1OSZJqYMYpSSpMGUq1ZpySJNXAwClJUg0s1UqSClOGUq2BU5JUGGfVSpLUYMw4JUmFsVQrSVINShA3LdVKklQLM05JUnFKkHIaOCVJhXFWrSRJDcaMU5JUGGfVSpJUgxLETUu1kiTVwoxTklScEqScZpySpMJEG/6vqteLGBIRUyNiWkSMWcbjH4iIG/PHJ0bEgNb6NHBKkkopIroCFwN7AIOAERExqFmzQ4HXU0obAT8BftRavwZOSVJhItruqMLWwLSU0tMppXeAG4BhzdoMA67Kv78Z2CWi5d69x9kGeqxUhqp98SJiVEppbEePo97Me+iijh5CXfLnrXNoy9+XETEKGFVxaWyz/8brAs9WnM8EtmnWzZI2KaWFEfEmsAbwyvJe14xTHWlU602kNuPPW8mklMamlLasOAr5w8jAKUkqq+eA/hXn/fJry2wTESsBqwKvttSpgVOSVFaTgIERsX5EdAf2B8Y1azMOOCj/fjjwp5RSaqlT73GqI3m/SUXy563B5PcsRwN3AF2BK1JKj0fE6cDklNI44HLgmoiYBrxGFlxbFK0EVkmSVMFSrSRJNTBwSpJUAwOnahIRAyIi5UdTRLwcEb+MiJXfR5/35v31acuxqn40+7n6XsX1yxdfb+X5p+bthufnKSIea+U5V+bttmybd6FGYeDUinoI+BJwHzAC+Or76Ov0vI/ZbTAu1b+RkVkZ+MIK9jECOK6VNpfm7Z5awddQgzJwakW9DNxFNt0boHdErBsRv46I1yPi+Yj4Yb5WJBExPSLeiojzI+K1iJgUEWvnzz0ZuD7vIyLi3Ih4IzV3dkMAAAaBSURBVCImRMQteVYwOO8nRcS/IuK6iHgzIv4YEb0Kfu9qP08DGwCDyYJmN/LP3UXEphExJSLm5j8f4yNi3eX0cz1wTv683hFxWf4zOTcirs3bfDVvt2He7vD8Z+utiHgwIrbLrzfPZh9bnAFHxMb5wuDz8p/7+9rh30SdjIFTK+ozwCzgB8DzwEXAtcBuwPlkn436NnBUxXN6Ab2B24EtgcOX0e9Q4JvAo8B1eX/NbUT2y/Sv+eP7ve93o87iCWAicEh+/BZ4I3/sHbI1Rb9G9vO2O3BqFX2eBxwB3A0cQxaclxIRO5N9XOVlsp+/9YBxEbFGK30fBWwFnACcCDxTxXhU5/wcp1bUROC7ZJnBScDxwI5ku+2dUtFuN+DC/Psmsr/yPw4cCAxYRr875V9PSyndFRGfAg5o1uaFlNIJEbE/2S/PZfWj+nUFcAHwAWAIcG5+/QNkPwubV7TdrIr+hpL9kXdQSqlpOW32zL+eklK6MyLWA74DfKqVvv9F9jO/J1n15fwqxqM6Z8apFfVKSuku4Pv5+c5kv0D+QRYsFx9nVDxnXkppPrAwP+/aQv8tTQZ5Lf9aTT+qPzcAi8gW5L6z4vpJZEFzDFnFYwHQo41fOzX7Sj4WeDfRWG1J45QuAnYlC5rDgAkRsXEbj0mdjBmnVtQ6eca3fX4+DXgT2CG/9hywHfBP3r0PWo17gGOBk/N985pvAaSSSynNjohDgDkppaZl7PC0BvA5svuf1bgVOBi4KiLuBT6cUjq5WZvxwLeA0yJiQ/I9GoEJwIfyNsPzTY6X3FeNiCOBPmQ//9PIAntfYGqVY1MdMnBqRW1BNrFiNtlyVseTZYDnAaPJfqk9ClxdY7+3Aj8mu7/VFbifrBz7RktPUrmklG5cxuXvAx8ju694PtkfatU4liw73Zvsfvgty3i9P+VbVJ1A9vM3BfhGSunViPg18GWyLPcd3p3ABPA2WVDuB8wh2zT5/irHpTrlknvqdCLim8AjwNpkvyDnARumlN7u0IFJEgZOdUJ5OW1rsr/uJwPHp5Qe6tBBSVLOwClJUg2cVStJUg0MnJIk1cDAKUlSDQycEu/ZnSPl6+neUMWSa9X0fVze58j8fHpE/KeV5/xPvkbqx1fg9Ybnr3fqMh4bnD92URX9tLrDSFs+T6oXfo5TWtpDwNnAcOCLwFtkH4ZfSkR0TSktan69SscA3Vtp8z9kSxdOBx5ewdeR1A7MOKWlPZ9Sup5sHV6AbWCp3V0uiYg3gc0i4tMR8deI+E9EPBkRIxZ3kmeZr0TEFN67nuqFZIuVExHdI+LMiHgm32HjL/lOMGfnbX+RZ3ADIuK/I+LOiJidt/9GxesdEBEvRMQzvLveb6si4rzI9lR9OyKejogjmjXpFhFX5+99fESsnj9vuWORys7AKS2tW0SsCeyTn8+oeKwXsA7ZPo+zgNvI1i39PllmeG1EfDwiPkYW+F4kW0lp1xZeb0x+PE624tLfyVatuS5//DKyPSNfB34HDALOIltk/8cRMTQi+gKXky2i/32yxfar9QTZGrDHAS8BF+cLnC/2EeDZfDx7AN+LiJWWN5YaXleqW5ZqpaUt3i4NsvV2v9Ps8YNSSm9GxGfJ1jD9ENnWaovtzLuLgv8kpXR5RPTn3Qy2uaFkC4p/MaU0Z/HFiHiYbKPwiSmlGyJiE2Bg/nDlwvm7kf0B3AO4IqU0NiIWAT+v8v1uQBawK/c0HcS7fzDMTCmdFBHdyUrWg4GNWxjLrVW+rlS3DJzS0iaSZWCvAVOaLfP3Vkqp+fqoVwPXVJxPBz6bfx/NvtZieSuT3EG+QXPuRfKNmGt9vYj4KNnarA+Tvef9yNYIrnbHkWWNRSo9A6e0tFdSSndX0e6vZMF1CNnuLysBe5FlYPfmbY6NiC5ki4Avz61km3rfGBE3A5unlI4lK80C7BERc4HfkO39uB3ZhsxzyUrAt5AFsPnAwRExg2yj51r0BPqz7JJyv4j4AdkOIF3y9za1hbE4m1al5z1OaQWklF4jC5TTgB+SZWxzgekppX+Q7RazNtkM2j+30NUP82NT4BLgE/n1ccDfyLLAX6aUFpJtsfYAWdn3DGAV4NGU0ktkZdQuwLfJtsKq5j38E/gJ2c4eo4HfL6PZk/njXwD+AJzR0liqeV2p3rlWrSRJNTDjlCSpBgZOSZJqYOCUJKkGBk5Jkmpg4JQkqQYGTkmSamDglCSpBv8PPvDCyBljvAUAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 504x504 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "Fold  4\n",
            "                   \n",
            "Epoch 1/50\n",
            "10/10 [==============================] - 20s 2s/step - loss: 0.5893 - accuracy: 0.7888 - val_loss: 0.4624 - val_accuracy: 0.8032\n",
            "Epoch 2/50\n",
            "10/10 [==============================] - 17s 2s/step - loss: 0.4575 - accuracy: 0.8045 - val_loss: 0.4047 - val_accuracy: 0.8428\n",
            "Epoch 3/50\n",
            "10/10 [==============================] - 18s 2s/step - loss: 0.3834 - accuracy: 0.8344 - val_loss: 0.3192 - val_accuracy: 0.8576\n",
            "Epoch 4/50\n",
            "10/10 [==============================] - 17s 2s/step - loss: 0.2939 - accuracy: 0.8716 - val_loss: 0.2046 - val_accuracy: 0.8699\n",
            "Epoch 5/50\n",
            "10/10 [==============================] - 17s 2s/step - loss: 0.1931 - accuracy: 0.9092 - val_loss: 0.1104 - val_accuracy: 0.9909\n",
            "Epoch 6/50\n",
            "10/10 [==============================] - 17s 2s/step - loss: 0.1148 - accuracy: 0.9758 - val_loss: 0.0630 - val_accuracy: 0.9973\n",
            "Epoch 7/50\n",
            "10/10 [==============================] - 17s 2s/step - loss: 0.0669 - accuracy: 0.9942 - val_loss: 0.0369 - val_accuracy: 0.9985\n",
            "Epoch 8/50\n",
            "10/10 [==============================] - 17s 2s/step - loss: 0.0405 - accuracy: 0.9970 - val_loss: 0.0224 - val_accuracy: 0.9989\n",
            "Epoch 9/50\n",
            "10/10 [==============================] - 18s 2s/step - loss: 0.0261 - accuracy: 0.9978 - val_loss: 0.0144 - val_accuracy: 0.9991\n",
            "Epoch 10/50\n",
            "10/10 [==============================] - 17s 2s/step - loss: 0.0180 - accuracy: 0.9983 - val_loss: 0.0097 - val_accuracy: 0.9992\n",
            "Epoch 11/50\n",
            "10/10 [==============================] - 17s 2s/step - loss: 0.0133 - accuracy: 0.9987 - val_loss: 0.0074 - val_accuracy: 0.9994\n",
            "Epoch 12/50\n",
            "10/10 [==============================] - 17s 2s/step - loss: 0.0106 - accuracy: 0.9988 - val_loss: 0.0060 - val_accuracy: 0.9995\n",
            "Epoch 13/50\n",
            "10/10 [==============================] - 17s 2s/step - loss: 0.0090 - accuracy: 0.9990 - val_loss: 0.0051 - val_accuracy: 0.9995\n",
            "Epoch 14/50\n",
            "10/10 [==============================] - 17s 2s/step - loss: 0.0078 - accuracy: 0.9992 - val_loss: 0.0044 - val_accuracy: 0.9996\n",
            "Epoch 15/50\n",
            "10/10 [==============================] - 18s 2s/step - loss: 0.0070 - accuracy: 0.9992 - val_loss: 0.0039 - val_accuracy: 0.9996\n",
            "Epoch 16/50\n",
            "10/10 [==============================] - 17s 2s/step - loss: 0.0063 - accuracy: 0.9993 - val_loss: 0.0035 - val_accuracy: 0.9996\n",
            "Epoch 17/50\n",
            "10/10 [==============================] - 17s 2s/step - loss: 0.0058 - accuracy: 0.9993 - val_loss: 0.0032 - val_accuracy: 0.9996\n",
            "Epoch 18/50\n",
            "10/10 [==============================] - 17s 2s/step - loss: 0.0054 - accuracy: 0.9994 - val_loss: 0.0030 - val_accuracy: 0.9996\n",
            "Epoch 19/50\n",
            "10/10 [==============================] - 17s 2s/step - loss: 0.0050 - accuracy: 0.9994 - val_loss: 0.0029 - val_accuracy: 0.9996\n",
            "Epoch 20/50\n",
            "10/10 [==============================] - 17s 2s/step - loss: 0.0048 - accuracy: 0.9994 - val_loss: 0.0027 - val_accuracy: 0.9996\n",
            "Epoch 21/50\n",
            "10/10 [==============================] - 17s 2s/step - loss: 0.0046 - accuracy: 0.9994 - val_loss: 0.0026 - val_accuracy: 0.9996\n",
            "Epoch 22/50\n",
            "10/10 [==============================] - 17s 2s/step - loss: 0.0044 - accuracy: 0.9994 - val_loss: 0.0024 - val_accuracy: 0.9996\n",
            "Epoch 23/50\n",
            "10/10 [==============================] - 19s 2s/step - loss: 0.0042 - accuracy: 0.9995 - val_loss: 0.0024 - val_accuracy: 0.9996\n",
            "Epoch 24/50\n",
            "10/10 [==============================] - 17s 2s/step - loss: 0.0041 - accuracy: 0.9995 - val_loss: 0.0023 - val_accuracy: 0.9996\n",
            "Epoch 25/50\n",
            "10/10 [==============================] - 17s 2s/step - loss: 0.0039 - accuracy: 0.9995 - val_loss: 0.0022 - val_accuracy: 0.9996\n",
            "Epoch 26/50\n",
            "10/10 [==============================] - 17s 2s/step - loss: 0.0039 - accuracy: 0.9995 - val_loss: 0.0022 - val_accuracy: 0.9996\n",
            "Epoch 27/50\n",
            "10/10 [==============================] - 18s 2s/step - loss: 0.0037 - accuracy: 0.9995 - val_loss: 0.0021 - val_accuracy: 0.9996\n",
            "Epoch 28/50\n",
            "10/10 [==============================] - 17s 2s/step - loss: 0.0036 - accuracy: 0.9995 - val_loss: 0.0021 - val_accuracy: 0.9996\n",
            "Epoch 29/50\n",
            "10/10 [==============================] - 17s 2s/step - loss: 0.0036 - accuracy: 0.9995 - val_loss: 0.0020 - val_accuracy: 0.9996\n",
            "Epoch 30/50\n",
            "10/10 [==============================] - 17s 2s/step - loss: 0.0036 - accuracy: 0.9995 - val_loss: 0.0020 - val_accuracy: 0.9996\n",
            "Epoch 31/50\n",
            "10/10 [==============================] - 17s 2s/step - loss: 0.0035 - accuracy: 0.9995 - val_loss: 0.0020 - val_accuracy: 0.9996\n",
            "Epoch 32/50\n",
            "10/10 [==============================] - 18s 2s/step - loss: 0.0034 - accuracy: 0.9996 - val_loss: 0.0019 - val_accuracy: 0.9996\n",
            "Epoch 33/50\n",
            "10/10 [==============================] - 18s 2s/step - loss: 0.0033 - accuracy: 0.9996 - val_loss: 0.0019 - val_accuracy: 0.9996\n",
            "Epoch 34/50\n",
            "10/10 [==============================] - 18s 2s/step - loss: 0.0033 - accuracy: 0.9995 - val_loss: 0.0019 - val_accuracy: 0.9996\n",
            "Epoch 35/50\n",
            "10/10 [==============================] - 17s 2s/step - loss: 0.0033 - accuracy: 0.9996 - val_loss: 0.0019 - val_accuracy: 0.9996\n",
            "Epoch 36/50\n",
            "10/10 [==============================] - 17s 2s/step - loss: 0.0033 - accuracy: 0.9996 - val_loss: 0.0018 - val_accuracy: 0.9996\n",
            "Epoch 37/50\n",
            "10/10 [==============================] - 17s 2s/step - loss: 0.0032 - accuracy: 0.9995 - val_loss: 0.0018 - val_accuracy: 0.9996\n",
            "Epoch 38/50\n",
            "10/10 [==============================] - 17s 2s/step - loss: 0.0032 - accuracy: 0.9996 - val_loss: 0.0018 - val_accuracy: 0.9996\n",
            "Epoch 39/50\n",
            "10/10 [==============================] - 17s 2s/step - loss: 0.0032 - accuracy: 0.9996 - val_loss: 0.0018 - val_accuracy: 0.9996\n",
            "Epoch 40/50\n",
            "10/10 [==============================] - 17s 2s/step - loss: 0.0031 - accuracy: 0.9996 - val_loss: 0.0018 - val_accuracy: 0.9996\n",
            "Epoch 41/50\n",
            "10/10 [==============================] - 17s 2s/step - loss: 0.0032 - accuracy: 0.9996 - val_loss: 0.0018 - val_accuracy: 0.9996\n",
            "Epoch 42/50\n",
            "10/10 [==============================] - 17s 2s/step - loss: 0.0031 - accuracy: 0.9996 - val_loss: 0.0018 - val_accuracy: 0.9996\n",
            "Epoch 43/50\n",
            "10/10 [==============================] - 17s 2s/step - loss: 0.0032 - accuracy: 0.9996 - val_loss: 0.0018 - val_accuracy: 0.9996\n",
            "Epoch 44/50\n",
            "10/10 [==============================] - 17s 2s/step - loss: 0.0031 - accuracy: 0.9995 - val_loss: 0.0018 - val_accuracy: 0.9996\n",
            "Epoch 45/50\n",
            "10/10 [==============================] - 17s 2s/step - loss: 0.0031 - accuracy: 0.9996 - val_loss: 0.0017 - val_accuracy: 0.9996\n",
            "Epoch 46/50\n",
            "10/10 [==============================] - 17s 2s/step - loss: 0.0031 - accuracy: 0.9996 - val_loss: 0.0017 - val_accuracy: 0.9996\n",
            "Epoch 47/50\n",
            "10/10 [==============================] - 17s 2s/step - loss: 0.0031 - accuracy: 0.9996 - val_loss: 0.0017 - val_accuracy: 0.9996\n",
            "Epoch 48/50\n",
            "10/10 [==============================] - 17s 2s/step - loss: 0.0030 - accuracy: 0.9996 - val_loss: 0.0017 - val_accuracy: 0.9996\n",
            "Epoch 49/50\n",
            "10/10 [==============================] - 17s 2s/step - loss: 0.0031 - accuracy: 0.9996 - val_loss: 0.0017 - val_accuracy: 0.9996\n",
            "Epoch 50/50\n",
            "10/10 [==============================] - 17s 2s/step - loss: 0.0031 - accuracy: 0.9996 - val_loss: 0.0017 - val_accuracy: 0.9996\n",
            "17675/17675 [==============================] - 31s 2ms/step - loss: 0.0017 - accuracy: 0.9996\n",
            "CV Evaluate: [0.0017248853109776974, 0.9996110200881958]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 864x432 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 504x504 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 504x504 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "text": [
            "5 Fold Cv Avg:  0.9986707334395375 List: [0.9996746679491351, 0.9998090438933829, 0.9995579719754233, 0.9947009680413739, 0.9996110153383725]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KtwlZJnrqB-s"
      },
      "source": [
        "historys = []\n",
        "for i in range(5):\n",
        "  import numpy as np  \n",
        "  historys.append(np.load('/content/drive/MyDrive/IDS__fold_' + str(i) + '.npy', allow_pickle=True).tolist())"
      ],
      "execution_count": 173,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "yIX-imU0VA_J",
        "outputId": "ea2354d8-81fe-4247-b824-93939edc3cdd"
      },
      "source": [
        "import numpy as np\n",
        "import cv2\n",
        "import os\n",
        "import pandas as pd\n",
        "import seaborn as sns\n",
        "import matplotlib.pyplot as plt\n",
        "import matplotlib.lines as mlines\n",
        "\n",
        "def plot_folds_history_acc(historys):\n",
        "\n",
        "  val_accuracy_1 = historys[0]['val_accuracy']\n",
        "  val_accuracy_2 = historys[1]['val_accuracy']\n",
        "  val_accuracy_3 = historys[2]['val_accuracy']\n",
        "  val_accuracy_4 = historys[3]['val_accuracy']\n",
        "  val_accuracy_5 = historys[4]['val_accuracy']\n",
        "  \n",
        "  epochs_range = range(50)\n",
        "\n",
        "  plt.figure(figsize=(20, 10))\n",
        "    \n",
        "  plt.plot(val_accuracy_1, 'ob')\n",
        "  plt.plot(val_accuracy_1, 'b')\n",
        "  b_circle_line = mlines.Line2D([], [], color='b', marker='o', label='Fold 1')\n",
        "\n",
        "  plt.plot(val_accuracy_2, 'og')\n",
        "  plt.plot(val_accuracy_2, 'g')\n",
        "  g_circle_line = mlines.Line2D([], [], color='g', marker='o', label='Fold 2')\n",
        "    \n",
        "  plt.plot(val_accuracy_3, 'or')\n",
        "  plt.plot(val_accuracy_3, 'r')\n",
        "  r_circle_line = mlines.Line2D([], [], color='r', marker='o', label='Fold 3')\n",
        "\n",
        "  plt.plot(val_accuracy_4, 'om')\n",
        "  plt.plot(val_accuracy_4, 'm')\n",
        "  m_circle_line = mlines.Line2D([], [], color='m', marker='o', label='Fold 4')\n",
        "\n",
        "  plt.plot(val_accuracy_5, 'oy')\n",
        "  plt.plot(val_accuracy_5, 'y')\n",
        "  y_circle_line = mlines.Line2D([], [], color='y', marker='o', label='Fold 5')\n",
        "\n",
        "  plt.grid(True)\n",
        "\n",
        "  plt.legend(loc='lower right', handles=[b_circle_line, g_circle_line, r_circle_line, m_circle_line, y_circle_line], title='AVG ' + str(round(cv_sum / 5 * 100, 2)), framealpha=1, shadow=True, borderpad=1)\n",
        "  plt.xlabel('Epochs')\n",
        "  plt.ylabel('Accuracy')\n",
        "    \n",
        "  plt.show()\n",
        "\n",
        "\n",
        "def plot_folds_history_lr(historys):\n",
        "\n",
        "  lr_1 = historys[0]['lr']\n",
        "  lr_2 = historys[1]['lr']\n",
        "  lr_3 = historys[2]['lr']\n",
        "  lr_4 = historys[3]['lr']\n",
        "  lr_5 = historys[4]['lr']\n",
        "\n",
        "  epochs_range = range(50)\n",
        "\n",
        "  plt.figure(figsize=(20, 10))\n",
        "    \n",
        "  plt.plot(lr_1, 'ob')\n",
        "  plt.plot(lr_1, 'b')\n",
        "  b_circle_line = mlines.Line2D([], [], color='b', marker='o', label='Fold 1')\n",
        "\n",
        "  plt.plot(lr_2, 'og')\n",
        "  plt.plot(lr_2, 'g')\n",
        "  g_circle_line = mlines.Line2D([], [], color='g', marker='o', label='Fold 2')\n",
        "    \n",
        "  plt.plot(lr_3, 'or')\n",
        "  plt.plot(lr_3, 'r')\n",
        "  r_circle_line = mlines.Line2D([], [], color='r', marker='o', label='Fold 3')\n",
        "\n",
        "  plt.plot(lr_4, 'om')\n",
        "  plt.plot(lr_4, 'm')\n",
        "  m_circle_line = mlines.Line2D([], [], color='m', marker='o', label='Fold 4')\n",
        "\n",
        "  plt.plot(lr_5, 'oy')\n",
        "  plt.plot(lr_5, 'y')\n",
        "  y_circle_line = mlines.Line2D([], [], color='y', marker='o', label='Fold 5')\n",
        "\n",
        "  plt.grid(True) \n",
        "\n",
        "  plt.legend(loc='lower right', handles=[b_circle_line, g_circle_line, r_circle_line, m_circle_line, y_circle_line], framealpha=1, shadow=True, borderpad=1)\n",
        "  plt.xlabel('Epochs')\n",
        "  plt.ylabel('Learning Rate')\n",
        "    \n",
        "  plt.show()\n",
        "\n",
        "\n",
        "def plot_folds_history_loss(historys):\n",
        "\n",
        "  loss_1 = historys[0]['loss']\n",
        "  loss_2 = historys[1]['loss']\n",
        "  loss_3 = historys[2]['loss']\n",
        "  loss_4 = historys[3]['loss']\n",
        "  loss_5 = historys[4]['loss']\n",
        "\n",
        "\n",
        "  epochs_range = range(50)\n",
        "\n",
        "  plt.figure(figsize=(20, 10))\n",
        "\n",
        "  plt.grid(True) \n",
        "  \n",
        "  plt.plot(loss_1, 'ob')\n",
        "  plt.plot(loss_1, 'b')\n",
        "  b_circle_line = mlines.Line2D([], [], color='b', marker='o', label='Fold 1')\n",
        "\n",
        "  plt.plot(loss_2, 'og')\n",
        "  plt.plot(loss_2, 'g')\n",
        "  g_circle_line = mlines.Line2D([], [], color='g', marker='o', label='Fold 2')\n",
        "    \n",
        "  plt.plot(loss_3, 'or')\n",
        "  plt.plot(loss_3, 'r')\n",
        "  r_circle_line = mlines.Line2D([], [], color='r', marker='o', label='Fold 3')\n",
        "\n",
        "  plt.plot(loss_4, 'om')\n",
        "  plt.plot(loss_4, 'm')\n",
        "  m_circle_line = mlines.Line2D([], [], color='m', marker='o', label='Fold 4')\n",
        "\n",
        "  plt.plot(loss_5, 'oy')\n",
        "  plt.plot(loss_5, 'y')\n",
        "  y_circle_line = mlines.Line2D([], [], color='y', marker='o', label='Fold 5')\n",
        "\n",
        "  plt.legend(loc='lower right', handles=[b_circle_line, g_circle_line, r_circle_line, m_circle_line, y_circle_line], framealpha=1, shadow=True, borderpad=1)\n",
        "  plt.xlabel('Epochs')\n",
        "  plt.ylabel('Tranining Loss')\n",
        "    \n",
        "  plt.show()\n",
        "\n",
        "def plot_folds_history_loss_val(historys):\n",
        "\n",
        "  val_loss_1 = historys[0]['val_loss']\n",
        "  val_loss_2 = historys[1]['val_loss']\n",
        "  val_loss_3 = historys[2]['val_loss']\n",
        "  val_loss_4 = historys[3]['val_loss']\n",
        "  val_loss_5 = historys[4]['val_loss']\n",
        "\n",
        "  epochs_range = range(50)\n",
        "\n",
        "  plt.figure(figsize=(20, 10))\n",
        "    \n",
        "  plt.plot(val_loss_1, 'ob')\n",
        "  plt.plot(val_loss_1, 'b')\n",
        "  b_circle_line = mlines.Line2D([], [], color='b', marker='o', label='Fold 1')\n",
        "\n",
        "  plt.plot(val_loss_2, 'og')\n",
        "  plt.plot(val_loss_2, 'g')\n",
        "  g_circle_line = mlines.Line2D([], [], color='g', marker='o', label='Fold 2')\n",
        "    \n",
        "  plt.plot(val_loss_3, 'or')\n",
        "  plt.plot(val_loss_3, 'r')\n",
        "  r_circle_line = mlines.Line2D([], [], color='r', marker='o', label='Fold 3')\n",
        "\n",
        "  plt.plot(val_loss_4, 'om')\n",
        "  plt.plot(val_loss_4, 'm')\n",
        "  m_circle_line = mlines.Line2D([], [], color='m', marker='o', label='Fold 4')\n",
        "\n",
        "  plt.plot(val_loss_5, 'oy')\n",
        "  plt.plot(val_loss_5, 'y')\n",
        "  y_circle_line = mlines.Line2D([], [], color='y', marker='o', label='Fold 5')\n",
        "\n",
        "  plt.grid(True) \n",
        "\n",
        "  plt.legend(loc='lower right', handles=[b_circle_line, g_circle_line, r_circle_line, m_circle_line, y_circle_line], framealpha=1, shadow=True, borderpad=1)\n",
        "  plt.xlabel('Epochs')\n",
        "  plt.ylabel('validation Loss')\n",
        "    \n",
        "  plt.show()\n",
        "\n",
        "\n",
        "plot_folds_history_acc(historys)\n",
        "plot_folds_history_lr(historys)\n",
        "plot_folds_history_loss(historys)\n",
        "plot_folds_history_loss_val(historys)"
      ],
      "execution_count": 172,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 1440x720 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 1440x720 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 1440x720 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 1440x720 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    }
  ]
}